“Hidden beneath the waves of chaos is a tapestry of synchronicity.
Our challenge is to ride the waves of chaos with purpose, trust, and agility allowing everything in its time to unfold and harmonize.”

A business comes into being thru interactions – with external influences like customers, partners, and suppliers, and with internal units and the team to provide a holistic view of its existence.

Just like in Physics where atoms are composed of constituent particles, or in the philosophy of Gestalt where the whole is greater than the sum of its parts, so to with enterprises, the units that aggregate must function effectively within and in relation to the others so that their interactions can completely help the effective and appropriate functioning of the enterprise.

A Large World Model (LWM) is a digital spatial representation of a physical scenario by creating complex architectural models, simulating real-world physics, and designing intricate products. At Numorpho Cybernetic Systems (NUMO), our basis is to understand cause and effect by assimilating digital threads and digital twins to automate, harmonize, and optimize operations to enable robust digital strategies and appropriate actionable outcomes.

In this article, we will evolve the second tenet of our process engineering platform to enable automation by composing virtual dynamic blueprints of physical processes.

The Digital Twine World Model (DTWM) is a new way of thinking about managing the process cycle. It is a strategy of thinking about and treating the end-to-end production process as a singular ecosystem – a system of cooperating, self-organizing entities that make the systems work. It is an attempt to design a platform for process engineering, linking people, organizations, data, processes, and machines in the new paradigm in the same way the Internet is a new way of thinking about the exchange of information.

Here is a succinct definition:

The Digital Twine World Model is a new paradigm for managing end-to-end production processes. It’s described as:

• A strategy for treating production processes as a singular ecosystem
• A system of cooperating, self-organizing entities
• A platform for process engineering that links people, organizations, data, processes, and machines

TABLE OF CONTENTS

• Enterprise Value Streams
• Large World Models
• The Genesis of the Digital Twine
  • Emmy Noether’s Math
  • The Incan Quipu
  • Feynman Diagrams
  • Subway Cartography
• Industry 4.0
• The Digital Twine World Model
  • Digital Threads and Digital Twins
  • Basis for the Digital Twine
  • A Dialog with Perplexity.ai
• Enterprise System Value Drivers
• Reference Architecture
• Summary
  • By Pi.ai

ENTERPRISE VALUE STREAMS

The key to building an ecosystem for Industry 4.0 is managing processes using the Digital Thread and enabling interaction with the systems using Digital Twin. This must happen in all phases of the ecosystem – Strategy, Innovation, Product Development, Smart Manufacturing, Logistics and Connected Commercials. Technology planning is not step one. First, companies need to have a good business strategy and tangible objectives. Once you have a good business strategy in place, what does that means for the product lifecycle process?

We call it the Digital Twine – an assemblage of people, processes and platforms that need to be orchestrated to achieve coordination, optimization, and proactive assertions. This is key for the building of the tapestry for all aspects of enterprise transformation to enable the activities to coordinate, blueprint and operate processes in engineering systems.

LARGE WORLD MODEL (LWM)

A Large World Model (LWM) is a digital spatial representation of a physical scenario by creating complex architectural models, simulating real-world physics, and designing intricate products.

Large World Models (LWMs) are an emerging type of artificial intelligence model that aims to understand and interact with the physical 3D world, similar to how Large Language Models (LLMs) process and generate text. Here are the key points about LWMs:

1. Purpose: LWMs are designed to “perceive, generate and interact with the 3D world,” giving AI systems spatial intelligence comparable to human understanding of the physical environment.
2. Analogy to LLMs: Just as LLMs process language, LWMs are developed to understand and manipulate the physical world. They aim to move AI beyond 2D representations to full 3D worlds, both virtual and real.
3. Data and Training: LWMs require quality data curation from the real world (e.g., video feeds, sensor data), fine-tuning on domain-specific datasets, and reinforcement learning from human feedback.
4. Potential Applications: LWMs could have applications in various fields, including robotics, design, gaming, visual effects, augmented reality (AR), and virtual reality (VR).
5. Current Development: Companies like World Labs are working on building LWMs. They’ve raised significant funding ($230 million) to develop this technology.
6. Capabilities: LWMs are expected to enable the generation of 3D interactive worlds, reasoning about physics and cause-and-effect relationships, and potentially revolutionizing industries from healthcare to manufacturing.
7. Challenges: Developing LWMs faces challenges similar to early LLM development, including the need for massive datasets, computational resources, and solving complex problems of spatial reasoning and interaction.
8. Future Potential: Researchers believe LWMs could unlock new capabilities for creative professionals and allow anyone to imagine and create their own 3D worlds, expanding the potential of generative AI beyond 2D images and videos.

In essence, Large World Models represent an ambitious effort to create AI systems that can understand, model, and interact with the physical world in three dimensions, potentially leading to significant advancements in AI’s ability to assist and augment human capabilities in the real world.

The journey from Large Language Models (LLMs) to Large World Models (LWMs) is reshaping the future of AI. While LLMs have revolutionized how we interact with text and 2D data, world models are introducing a new era of spatial intelligence—one where AI can predict, interact, and simulate complex 3D environments.

The potential for spatial intelligence is enormous, particularly in engineering and design, where artificial agents can now simulate millions of scenarios to optimize actions. This advancement brings us one step closer to AI systems that are as reliable and adaptive as the human mind.

THE GENESIS OF THE DIGITAL TWINE

In building a house, knitting a sweater, or writing a symphony, guiding concepts are formed into specific images which always involved the materials to be used. My methods are really methods of working and thinking, this is why they have crept in everywhere – Emmy Noether

The mathematical analysis of the construction of laws of physics began in the late 19th century as part of attempts to axiomatize physics. The most famous of these attempts, by Emmy Noether whose theorem demonstrated that each symmetry of the laws of physics corresponds to a fundamental conserved quantity, such as energy or momentum. Noether’s idea that equations are the key to understanding physics has guided mathematicians, physicists, and computer scientists ever since.

Noether saw math as what are now called structures. To her, the characteristics of a structure’s components — be they numbers, polynomials, or something else — mattered less than the networks of relations among an entire set of objects. This enabled her to give proofs that applied to more general structures than the original ones, and which revealed unseen connections. In evolving the framework for enterprise interactions, we will take a page from Noether’s book to fully represent the interactions between the people, process, and technology.

We will also draw equivalences from ancient calculating techniques – the Incan quipu, Feynman’s unique diagram to represent particle interactions in physics, and the way we generate cartography for subway transit systems to drive the basis for showcasing system interactions in an enterprise. Using the current definitions of digital thread and digital twine, we will compose a pipeline of intertwining threads – our definition of the Digital Twine to create a comprehensive reference architecture that will be used to compose digital blueprints for product development, manufacturing, and aftermarket. 

THE INCAN QUIPU

The Incas were a culture, a civilization, and a state. If a word were chosen to condense the substance of the Incas, it would have to be order. Almost everything – planning of crops, mining, behavior of armies, and transfer of power was orderly. The Incas did not have a written language. Nonetheless, they adopted a unique system of recording information from their predecessors. This ancient “operating system,” called quipus, dates back to 2600 BCE. “They were like early computers, early counting machines,” says author and four-time Emmy-award winning documentary filmmaker Kim MacQuarrie. Quipus were a system of knotted strings that stored data and communicated information. Cultures across the ancient Andean world used this system for thousands of years.

Quipus (kee-poo), sometimes called talking knots, were recording devices used by the Inca Empire, the largest empire in pre-Columbian America. The word quipu comes from the Quechua word for “knot.” A quipu usually consisted of colored, spun, and plied thread or strings from llama hair. Historic documents indicate that quipus were used for record keeping and sending messages by runner throughout the empire. The color, knot placement and type of cord are all part of the logical numerical reading of the quipu. Some quipus, called narrative quipus, are believed to have possibly encoded phonetic information but have not yet been deciphered.

Quipus proved extremely useful to the Incas as their rule expanded across the region. “What the Incas would do if they conquered an area was go in and the first thing that they would do is send their accountants, their inventory people,” MacQuarrie says. “They would go in and literally count all of the different resources: the streams, the fields, they’d measure the fields, the people, the sexes of the people, mining, fishing, whatever. They would keep records of it and they would take that information back to Cusco and they would make decisions on how to administer that area.”

Quipu use a wide variety of colors, strings, and sometimes several hundred knots all tied in various ways at various heights. These combinations can even represent, in abstract form, key episodes from traditional folk stories and poetry. In recent years scholars have also challenged the traditional view that quipu were merely a memory aid device and go so far as to suggest that quipu may have been progressing towards narrative records and so becoming a viable alternative to written language just when the Inca Empire collapsed. 

FEYNMAN DIAGRAMS

Physmatics, Swerve https://iep.utm.edu/epicur/

Physics is the study of matter, energy, and the interactions between them. It is the most fundamental of the sciences, and its discoveries have led to advances in all other scientific disciplines. Physics sees the big picture as well as the small. It looks at things in terms of the interactions between particles, but also in terms of the behavior of large groups of particles. Quantum physics describes the workings of the Universe at the level of the tiny particles, such as electrons and photons of light, that make it up. Most of our everyday experiences come from the interaction of matter particles with each other and with light, which can be explained by quantum electrodynamics (QED).

Richard Feynman’s eponymous diagrams not only illustrate complex particle interactions, but can make calculations easier, too.

This detailed theory would win the Nobel Prize in Physics for Richard Feynman, Julian Schwinger, and Sin-Itiro Tomonaga. Much of their original thinking involved complex mathematics, as it was necessary to deal with a vast number of potential interactions, each with different probabilities.

But Feynman realized that these interactions could be represented by simple diagrams – patterns that define the interplay of light and matter. Not only did they make QED more comprehensible, but Feynman diagrams also provided a visual tool for making otherwise impossible calculations practical.

In theoretical physics, a Feynman diagram is a pictorial representation of the mathematical expressions describing the behavior and interaction of subatomic particles. The scheme is named after American physicist Richard Feynman, who introduced the diagrams in 1948. Feynman diagrams are graphs that represent the interaction of particles rather than the physical position of the particle during a scattering process. The interaction of subatomic particles can be complex and difficult to understand; Feynman diagrams give a simple visualization of what would otherwise be an arcane and abstract formula.

In the diagram showcased alongside, an electron (e⁻) and a positron (e⁺) annihilate, producing a photon (γ, represented by the blue sine wave) that becomes a quark–antiquark pair (quark q, antiquark q̄), after which the antiquark radiates a gluon (g, represented by the green helix).

Left to right denotes the time dimension and bottom to top is the distance or space dimension. Thus, Feynman diagrams can be described as a graphical representation of perturbative contributions (The S-Matrix) to the transition amplitude or correlation function of a quantum mechanical or statistical field theory. The S-matrix or scattering matrix relates the initial state and the final state of a physical system undergoing a scattering process.

Feynman diagrams are pictures of processes that happen in space and time. What they show are not rigid geometric trajectories, but more flexible, “topological” constructions, reflecting quantum uncertainty. The Feynman Diagrams revolutionized particle physics by providing a simple system to sort out the infinite possibilities when elementary particles interact. This incredible simplicity provides insights into the nature of reality. While the diagrams are applied primarily to quantum field theory, they can also be used in other fields, such as solid-state theory. In this treatise, we will showcase how we use a similar interaction model to depict processes in an industrial setting to enable understanding and optimization of activities upstream, midstream, and downstream. 

SUBWAY CARTOGRAPHY

Now recognized across the world, the Tube map was originally the brainchild of Underground electrical draughtsman, Harry Beck, who produced this imaginative and beautifully simple design back in 1933.

Rather than emphasising distance and geographical accuracy, like other maps, Beck based his on the circuit diagrams he drew for his day job; stripping the sprawling Tube network down to a neat diagram of coloured, criss-crossing lines.

Beck’s map was initially rejected by the publicity department because it was considered too radical, but a successful trial print-run showed that it was just what the public wanted.

The result was an instantly clear and comprehensible chart that would become an essential guide to London – and a template for transport maps the world over.

Beck’s revolutionary design, with certain modifications and additions, survives to the present day and is set to serve London Underground and its millions of customers for many years to come.

Mapping the transient and non-transient, the tangible and the intangible aspects of Industry 4.0 requires creating a dynamic reference architecture that accounts for all of the different value streams (business drivers) that need to conjoin to create a collaborative ecosystem for managing the processes.

Using cartography techniques we will showcase how we use the Digital Twine, a combination of the Digital Thread and the Digital Twin to create a “transit map” for the different waypoints (intersections for integration) that will be needed to coordinate upstream processes (ideation, design and product development) with midstream activities (procurement, manufacturing and supply chain) and downstream aspects (customer enablement, marketing, sales, support and service) to achieve a holistic composition for all the needs for Industry 4.0 and beyond.

This will be used as a basis to define the Integration protocols to securely and efficiently manage intelligent data and information transfers utilizing a new API Gateway technique called Tendril Technology that will be detailed in subsequent posts.

INDUSTRY 4.0

The fourth industrial revolution, or Industry 4.0, revolves around automation and data exchange, and a wide range of manufacturing technologies form the core of its functioning in the world of business.

Industry 4.0 is not simply the act of replacing people with machines and robotics, instead it is the way in which people can work together with current automation and data exchange technologies such as cyber-physical systems, the Internet of Things (IoT), Operational Technology, cloud computing and cognitive computing. For the manufacturing industry, there are several key influencers driving the transition, including the need for data driven decisions with the use of IIoT, richer visual and collaborative business intelligence, and increased connectivity.

The Industry 4.0 toolset includes:

• Cyber Physical System (CPS) — a connected, automated device, capable of learning from and effecting the physical environment, that is intelligent and responsive and can act independently (e.g., self-diagnosing problems) or interdependently with humans or other CPSs to achieve efficiencies or resolve issues.

• Internet of Things (IOT) — the network over which CPSs can connect to the internet and to each other in a secure, auditable manner.

• Internet of Services (IOS)—when devices are networked over the IOT, new services focusing on logistics, intelligence, automation, and prediction are possible.

• Smart Factory—the combination of cyber-physical systems and humans, connected through the internet of things with support from the internet of services, monitor production processes, and make de-centralized decisions as part of an interdependent network. The factory management is orchestrated via smart enterprise resource planning (SERP) systems and supported by human and virtual agents to develop product that is responsive in real time to demand, market conditions, and value chain (e.g., logistics) feedback.

While many manufacturers are still busy developing methods for interconnecting new technologies to improve efficiency and productivity — the guiding principle behind Industry 4.0 — the next phase of industrialization is already upon us.

THE DIGITAL TWINE WORLD MODEL

BASIS FOR THE DIGITAL TWINE

Tools that gather data from disparate places and present it in a single, consumable form, in an accessible manner, help move the needle toward a unified network view for all teams; as mentioned, it’s a useful metaphor for a goal to work toward. We believe that the following three key areas will be important in the future as we continue to strive to achieve the ultimate goal of a fully connected product and service continuum:

1. Platform Integration and Management: We need to be able to manage massive amounts of data from various sources, components, and devices seamlessly through the digital thread to realize the connected value stream through the product and service continuum.
2. Intelligent Data Analytics: We need to be able to make sense of all the data that is being generated in order to make better decisions about what needs to be done with the product or service.
3. Connected Services and Applications: We need to be able to provide new services and applications to our customers in order to improve their experience and get more value out of our platform.

The key to building an ecosystem for Industry 4.0 is:

• managing processes using the digital thread, and
• enabling interaction with the systems using digital twin.

This must happen in all phases of the ecosystem – Strategy, Innovation, Product Development, Smart Manufacturing, Logistics and Connected Commercials. Technology planning is not step one. First, companies need to have a good business strategy and tangible objectives. Once you have a good business strategy in place, what does that means for the product lifecycle process? 

DIGITAL THREAD

A digital thread creates a closed loop between digital and physical worlds, transforming how products are engineered, manufactured, and serviced. Digital threads seek to create simple universal access to data and its lineage. They follow a single set of related data as it weaves in and out of business processes and functions to enable continuity and accessibility.

• https://www.ptc.com/en/blogs/corporate/what-is-a-digital-thread

The two primary value drivers of the digital thread are streamlining engineering and creating a digital continuity that are means to significant business improvements. Benefits of utilizing digital threads include improves quality, greater efficiency, lead-time reduction, enhanced innovation, and more.

Digital threads are created by linking together data from different sources. The data can be stored in a variety of formats, such as text, images, and 3D models. The data can be stored in a central repository or in a distributed system. Digital threads are used in a variety of industries, such as automotive, aerospace, and healthcare. Applications of digital threads include product lifecycle management, supply chain management, and product development. Digital threads can be used to create virtual prototypes, which can be used to test and validate products before they are manufactured. Digital threads can also be used to track the progress of products through the supply chain and to monitor the performance of products after they are sold.

WHAT IS A DTWM?

We call our second tenet the Digital Twine World Model (DTWM)^TM reference architecture – an assemblage of people, processes and platforms that need to be orchestrated to achieve coordination, optimization, and proactive assertions for the building of the tapestry for all aspects of Industry 4.0 and its progression to Industry and Services 5.0.

• collaboration between disparate and heterogeneous systems,
• coordination of digital threads comprising of historical and real time data, and
• manifestation of digital twins to enable physical-digital interrelationships, and
• management – the governing of people, process and technology

This enables blueprinting of initiatives in Industry 4.0 and beyond to enable processes to be automated, intelligent and connected.

We propose the Digital Twine as a fusion of the Internet of Things, Blockchain and other emerging technologies with a production ecosystem in a Cyber-Physical environment. By showcasing its adaptability to different domains, we will prove that it is not bound to any specific industry but can be extended and adapted to varied situations. It is a model for thinking about the production process in a new way.

https://ipfabric.io/blog/network-automation-buzzwords/ Data democratization, Intent based networking, Single Pane of Glass

• Data democratization refers to making network data, or information about your network, freely accessible to anyone in an organization who might need it beyond just the team working directly with the network (e.g., security teams, cloud teams, C-suite). The benefits include reduction of bottlenecks in workflows through self-service processes, enabling asynchronous work, harmony across teams, and reduced MTTR.
• The ambition of a digital twin – that is, an exact virtual replica of your network you can use to simulate and test changes – is sound, in that having a true digital twin would be extremely useful. Real-time updating of this network representation to reflect your actual network state should mean it’s always accurate and behaving as your real-life network does. However, we know that reality is not so. The issue here is not with the concept – if you can find a true digital twin, sign us up – but the term is often confidently applied to products and platforms that are not a digital twin at all. Generously, some may be a digital cousin, in the sense they share some DNA with your network but fundamentally, they won’t behave exactly the same under the same conditions (which is the whole point).
• This refers to decisions about – and changes to – the network being led by intent, or a defined set of business objectives that represent how you desire your network to operate. By starting with intent, usually stored in a Source of Truth repository, like the open-source Netbox, and having every network operation be in service of aligning with that intent in an automated fashion, you are ever closer to having your actual network state match your dream network state. Intent-based networking is largely attractive to enterprises because it can help manage the complexity inherent in a modern, dynamic network.
• A Single Source of Truth – that all teams can trust – is touted as a key element of network automation projects, especially so for intent-based networking. By nature, IBN requires that you express a single, consistent intent against which you build, test, and validate your network state. Your source of truth is the ultimate repository of your network desires that are determined by clear business goals, which your actual network state should be continuously validated against.

Follow the links on the right to know more about the DTWM

FRAMEWORK

An enterprise is an aggregation of people, processes and platforms that come together to provide for the basis for the company as shown in the diagram above. In this article we will talk about our Reference Architecture for Industry and Services 5.0, the Digital Twine that would enable coordination of processed in the enterprise and how it enables blueprinting use cases for brownfield, greenfield and bluesky initiatives end-to-end. It will correspond to the three remaining steps in our five-pronged approach of make, manage, move, market, and maintain.

Knitting the cybernetic fabric entails creating an end-to-end ecosystem of coordinating, processes, and the product pipeline. Part of the interaction takes place through physical production (the operational domain), and the other part of it is in the digital world (the information processing domain). It is this mélange between OT and IT that needs to be correctly ascertained for optimization and harmonization of processes, and to enable the creation of truly disruptive products and services.

The Digital Twine Reference Architecture shown below enables the themed aggregation of Digital Threads and Digital Twins encompassing all the pertinent enterprise systems to provide for the holistic progression of automated processes:

The key to building products and services in Industry 4.0 is managing processes using the Digital Thread and enabling interaction with the systems using Digital Twin. This must happen in all phases of the ecosystem – Strategy, Innovation, Product Development, Smart Manufacturing, Logistics and Connected Commercials. Digital Twine World Model (DTWM)^TM for process automation is our reference architecture to aggregate people, process, and technology for enabling Industry and Services 5.0. This combination of Digital Threads and Digital Twins spanning across all different enterprise systems, has been utilized to blueprint use cases ranging from brownfield, greenfield to blue-sky initiatives to enable make, manage, move, market, and maintain activities,

AKA the Digital Twine, a combination of Digital Threads and Digital Twins, our Reference Architecture would enable cyber-physical interactions at every aspect of the process, be it:

• upstream during ideation and product development,
• midstream to enable smart and advanced manufacturing techniques or
• downstream to enable rich customer experiences.

The diagram above represents a high-level schema of how we plan to enable this technological revolution. It will account for the complete management of data and information, account for the IT/OT convergence and instilling actionable intelligence in both the ecosystem as well as the products and services we create. A subsequent article on the 5th order of Cybernetics will detail out the basis behind “actionable intelligence” that will enable progression beyond current “brittle” AI trained networks to more robust, contextual, and pragmatic nexus.

We call it the Digital Twine – an assemblage of people, processes and platforms that need to be orchestrated to achieve coordination, optimization, and proactive assertions for the building of the tapestry for all aspects of Industry 4.0.

Arrange-Act-Assert: A Pattern for Writing Good Tests | Automation Panda

The Pattern

Arrange-Act-Assert is a great way to structure test cases. It prescribes an order of operations:

1. Arrange inputs and targets. Arrange steps should set up the test case. Does the test require any objects or special settings? Does it need to prep a database? Does it need to log into a web app? Handle all of these operations at the start of the test.
2. Act on the target behavior. Act steps should cover the main thing to be tested. This could be calling a function or method, calling a REST API, or interacting with a web page. Keep actions focused on the target behavior.
3. Assert expected outcomes. Act steps should elicit some sort of response. Assert steps verify the goodness or badness of that response. Sometimes, assertions are as simple as checking numeric or string values. Other times, they may require checking multiple facets of a system. Assertions will ultimately determine if the test passes or fails.

A DIAGLOG WITH PERPELXITY.AI

At Numorpho Cybernetic Systems, we are building a process engineering platform that intelligently interacts with the physical world using virtual manifestations via Digital Twins, AR/VR and AI. It will be based on a reference architecture called Digital Twin World Model to orchestrate such interactions where digital threads and digital twins will comprise of the fabric of the data engineering tapestry to connect the dots between people, process and technology.

Based on your description, it sounds like Numorpho Cybernetic Systems is developing an advanced process engineering platform that integrates several cutting-edge technologies:

It’s worth noting that while some of these applications are specifically mentioned for Toyota, others are common uses of digital twins in manufacturing and supply chain management that Toyota is likely to employ given their advanced use of this technology.

Industrial Metaverse is a term that was used in the recent past. How could Numorpho use LWMs and its DTWM to compose the Industrial Metaverse?

Based on the concept of Large World Models (LWMs) and Digital Twin World Model (DTWM), Numorpho could leverage these technologies to compose an Industrial Metaverse in the following ways:

1. Integration of LWMs and DTWM:
   • Use LWMs to create a comprehensive 3D understanding of the industrial environment, including physical spaces, equipment, and processes.
   • Implement the DTWM’s upstream, midstream, and downstream approach to structure the industrial metaverse, ensuring a clear progression from design to implementation to operation.
2. Digital Twin Creation:
   • Utilize OpenUSD and NVIDIA Omniverse to create detailed digital twins of industrial assets, processes, and environments.
   • Leverage the interoperability of OpenUSD to integrate data from various tools like Ansys, Autodesk, and Siemens, creating a comprehensive digital representation.
3. Real-time Data Integration:
   • Implement IoT sensors and edge computing to feed real-time data into the digital twins, ensuring the virtual environment accurately reflects the physical world.
   • Use this data to update LWMs continuously, improving their accuracy and predictive capabilities.
4. Simulation and Optimization:
   • Leverage the LWMs’ ability to understand and interact with the 3D world to run complex simulations across the entire industrial process.
   • Use the DTWM structure to simulate changes in upstream processes and predict their impact on midstream and downstream operations.
5. Collaborative Environment:
   • Create a shared virtual space where teams can interact with digital twins and LWMs in real-time, regardless of physical location.
   • Enable cross-functional collaboration by allowing different departments to visualize and manipulate the same digital models simultaneously.
6. Predictive Analytics and AI Integration:
   • Implement AI and machine learning algorithms to analyze data from the digital twins and LWMs, providing predictive insights and optimization recommendations.
   • Develop deep learning surrogates (DLS) based on the rich data environment provided by the industrial metaverse.
7. Training and Skill Development:
   • Use the immersive environment of the industrial metaverse for virtual training sessions, allowing employees to interact with digital replicas of equipment and processes safely.
8. Supply Chain Visualization:
   • Extend the industrial metaverse to encompass the entire supply chain, creating a comprehensive view of operations from raw materials to end products.
9. Product Lifecycle Management:
   • Utilize the DTWM’s three-pronged approach to manage the entire product lifecycle within the industrial metaverse, from initial design (upstream) through production (midstream) to distribution and support (downstream).
10. Sustainability Monitoring:
    • Incorporate sustainability metrics into the industrial metaverse, allowing for real-time monitoring and optimization of energy use and environmental impact across all stages of the DTWM.

By combining LWMs and DTWM within the framework of an industrial metaverse, Numorpho could create a powerful platform for industrial digitalization. This approach would enable comprehensive simulation, real-time optimization, and collaborative problem-solving across the entire industrial ecosystem, potentially revolutionizing how industrial processes are designed, implemented, and managed.

How can AR/VR and Deep Learning Surrogates play a part in DTWM and the overall strategy for LWM and the Industrial Metaverse?

Based on the concepts of Digital Twin World Model (DTWM), Large World Models (LWMs), and the Industrial Metaverse, AR/VR and Deep Learning Surrogates (DLS) can play significant roles in the overall strategy:

1. Augmented Reality (AR) and Virtual Reality (VR):

• • Immersive Visualization: AR/VR can provide immersive interfaces to interact with the DTWM across upstream, midstream, and downstream segments. This allows users to visualize complex data and processes in 3D space.
  • Training and Simulation: VR environments can be used for training personnel on complex industrial processes or equipment operation without risk to physical assets.
  • Remote Collaboration: AR/VR enables remote teams to collaborate in shared virtual spaces, interacting with digital twins and LWMs in real-time.
  • Maintenance and Operations: AR can overlay digital information onto physical equipment, assisting with maintenance procedures and operational guidance.

2. Deep Learning Surrogates (DLS):

• • Real-time Optimization: DLS can provide rapid approximations of complex simulations, enabling real-time decision-making and optimization within the Industrial Metaverse.
  • Predictive Analytics: By learning from vast amounts of data in the DTWM, DLS can offer predictive insights across the entire industrial process.
  • Model Order Reduction: DLS can help reduce the complexity of high-fidelity models, making them more suitable for real-time applications in the Industrial Metaverse.
  • Bridging Physical and Virtual: DLS can help in translating between physical sensor data and virtual representations, enhancing the accuracy of digital twins.

Integration in DTWM and LWM Strategy:

1. Upstream:
   • Use AR/VR for conceptual design and prototyping
   • Employ DLS for rapid design space exploration and optimization
2. Midstream:
   • Utilize AR for assembly and production guidance
   • Implement DLS for real-time process optimization and quality control
3. Downstream:
   • Deploy VR for customer experience and product showcasing
   • Apply DLS for predictive maintenance and performance optimization

Overall Strategy for Industrial Metaverse:

1. Data Integration: Use AR/VR and DLS to integrate and visualize data from various sources, creating a comprehensive digital representation of industrial processes.
2. Real-time Simulation: Leverage DLS to enable real-time, physics-based simulations within the Industrial Metaverse, allowing for interactive decision-making.
3. Human-Machine Interaction: Utilize AR/VR interfaces to enhance human interaction with LWMs and digital twins, bridging the gap between physical and virtual worlds.
4. Continuous Learning: Implement DLS to continuously learn from the vast data generated in the Industrial Metaverse, improving model accuracy and predictive capabilities over time.
5. Scalability: Use DLS to create scalable representations of complex systems, allowing the Industrial Metaverse to handle increasing levels of detail and complexity.

By integrating AR/VR and DLS into the DTWM framework and LWM strategy, Numorpho can create a more interactive, efficient, and intelligent Industrial Metaverse. This approach enables real-time decision-making, enhances collaboration, and provides deeper insights across the entire industrial lifecycle.

What are the benefits of using Deep Learning Surrogates in digital twin modeling?

There are several key benefits of using Deep Learning Surrogates (DLS) in digital twin modeling:

1. Reduced Computational Complexity: DLS can significantly reduce the computational complexity of simulations, allowing for faster processing and analysis of digital twin models.
2. Increased Speed: Deep learning models typically operate orders of magnitude faster than traditional physics-based simulations, enabling real-time or near real-time analysis and decision-making.
3. Efficient Approximation: DLS serve as efficient approximations of costly simulations, allowing for rapid evaluation of complex functions that would otherwise require significant computational resources.
4. Real-time Optimization: The speed and efficiency of DLS enable real-time optimization and decision-making within the Industrial Metaverse and digital twin environments.
5. Handling Uncertainty: DLS can handle uncertainty and complex behavior of systems, making them well-suited for modeling real-world scenarios.
6. Easy Integration with Optimization Algorithms: Surrogate models, including DLS, can be easily utilized in optimization and control algorithms.
7. Balancing Accuracy and Computational Power: DLS provide a balance between the level of accuracy and the computational power necessary, making them suitable for digital twin applications.
8. Extrapolation Capabilities: DLS can support problem-specific utilization and extrapolation, which is crucial for many control and optimization algorithms.
9. Adaptability: Deep learning surrogates leverage the power of deep neural networks, making them highly flexible and adaptable to various types of data and scenarios.
10. Capturing Complex Patterns: DLS can capture intricate patterns and relationships in data, potentially uncovering insights that might be missed by traditional modeling approaches.
11. Accelerated Design Optimization: DLS technology promises to accelerate and enhance automated design optimization, even for larger and more complex engineering challenges.

By incorporating Deep Learning Surrogates into digital twin modeling, organizations can create more efficient, responsive, and insightful virtual representations of physical systems, enabling better decision-making and optimization across various industries and applications.

How do Deep Learning Surrogates compare to traditional physics-based models in terms of accuracy?

Based on the information provided, Deep Learning Surrogates (DLS) compare to traditional physics-based models in the following ways in terms of accuracy:

1. Comparable Accuracy: In many cases, DLS can achieve accuracy levels comparable to traditional physics-based simulations. For example:
   • In inertial confinement fusion simulations, a DLS was found to be “nearly indistinguishable from the simulator in errors and expected quantities of energy yield”
   • For pipeline flow prediction, a DLS achieved very low relative errors compared to traditional simulations (e.g. median relative errors of 0.56% for temperature, 0.79% for pressure).

2. Potential for Higher Accuracy: In some cases, DLS may even outperform traditional models:
   • The DLS for inertial confinement fusion was “more accurate than other types of surrogate models”.
   • For airfoil flows, NN-based surrogate models have achieved “accuracies required for real-world, industrial applications”.

3. Accuracy-Speed Tradeoff: DLS can provide a good balance between accuracy and computational speed:
   • They offer “a balance between the level of accuracy and the computational power necessary”
   • DLS can operate “orders of magnitude faster than traditional physics simulations” while maintaining good accuracy.

4. Complex Pattern Capture: DLS can potentially capture intricate patterns that might be missed by traditional approaches:
   • They can “capture intricate patterns and relationships in data”.

5. Limitations in Physical Interpretability: While accurate, DLS may lack the physical interpretability of traditional models:
   • They are often considered “black-box” models, making it challenging to understand why they make specific predictions.

6. Dependence on Training Data: The accuracy of DLS is heavily dependent on the quality and quantity of training data:
   • They “demand substantial volumes of training data” from high-fidelity simulations.

In summary, Deep Learning Surrogates can often achieve accuracy comparable to or sometimes exceeding traditional physics-based models, especially when well-trained on high-quality data. However, this comes with tradeoffs in terms of physical interpretability and dependence on training data quality. The choice between DLS and traditional models often depends on the specific application requirements for accuracy, speed, and interpretability.

ENTERPRISE SYSTEMS AND VALUE DRIVERS

As we embark on building processes for Industry 4.0 and beyond that include emergent technologies like DARQ, there is need to reassess the simplistic integration maps that we developed for IT to create flexible, contemplative, and smart transfer mechanisms that include nuances from AI, Quantum Computing, Cybernetics and Distributed ledger transactions. In defining the reference architecture, the blueprints for use cases and in creating the integration framework for Industry 5.0, we will employ the model used in the Feynman Diagrams to simply represent complex interactions.

The DTWM^TM facilitates the design, build, evolution, and harmonization of activities demanding innovation, automation, and enablement across all the realms of the enterprise and its connected ecosystem. It does so by coordinating the physical and digital realms in in 3 facets:

1. Standardize & Manage – By building a correct foundation to store core master data, enable financial consolidation and justify progression.
2. Enable & Optimize – Integrate business processes to facilitate operations and the appropriate movement of physical goods and services.
3. Protect & Grow – Coordinate user interactions using online channels and API services to manage information flow to mature the business.

The Value Drives of an Enterprise are depicted below:

Agentic interactions between these value drivers can be orchestrated to streamline communication, optimize resource allocation, and enhance decision-making capabilities across various enterprise functions. Here’s how agentic interactions could be implemented for each value driver:

1. Flexible ERP: An ERP agent can interact with other agents to manage human and capital resources efficiently, adapting to changes in demand and supply.
2. Adaptive Engineering: An engineering agent can collaborate with manufacturing and data science agents to optimize product lifecycle management, ensuring continuous improvement and innovation.
3. Smart Manufacturing: A manufacturing agent can work with ERP, engineering, and commerce agents to manage demand, supply, and logistics, optimizing production throughput and minimizing waste.
4. Data Sciences and IT: A data science agent can interact with other agents to gather, analyze, and disseminate information, enabling data-driven decision-making across the enterprise.
5. Actionable Intelligence: An AI agent can integrate with other agents to provide smart, quick, appropriate, and equitable responses to various business scenarios, driving operational efficiency and agility.
6. Connected Commerce: A commerce agent can collaborate with manufacturing, data science, and AI agents to enable sales, marketing, and customer engagement, fostering business growth and competitiveness.
7. Distributed Ledger: A ledger agent can interact with other agents to ensure the immutability and trustworthiness of transient data, enhancing transparency and security across the enterprise.

By establishing agentic interactions between these value drivers, your enterprise can create a dynamic, interconnected ecosystem that fosters collaboration, innovation, and continuous improvement.

Moving to Industry and Services 5.0

1. Interoperability — networks of people and machines able to communicate via common standards-based data exchange infrastructure.
2. Virtualization — thanks to the interoperability of many context-aware devices it would then be possible to create a virtual copy of the world to assist the orchestration of the production process.
3. Decentralization — moving from the production line mass replication model to one in which autonomous decisions can be made, linking customers and market data into the production process, to allow for mass customization.
4. Real-Time Capability — if all parts of the value chain are connected, then the overall model will be able to change in real time.
5. Service Orientation — a shift away from products to the use of data to create new services that are more responsive to changing market needs.
6. Modularity — moving to a more flexible and agile way of configuring production capabilities, being able to rearrange loosely coupled modules to respond to changing needs.
7. Safety, security, resilience — these concepts are “anchored within the legal requirements of most countries with regard to patient safety and privacy protection. It is subsequently embedded in the clinical governance rules.”

We follow Behavior-Driven Development as we progress through building the ecosystem for the digital twine using the Make-Manage-Move-Market-Maintain theme.

REFERENCE ARCHITECTURE

Our reference architecture, the DTWM^TM enables the composition of new architectures that are needed for more real time, secure and frictionless interactions by coordinating all the streams that account for all interactions between the different processes in an enterprise.

The diagram below shows the composition of the DTWM reference architecture that corresponds with the different enterprise system value drivers to create a holistic view of all processes and their interactions.

It is based on the four themes: Design, Build, Evolve and Harmonize to collaborate between congruent streams of enterprise activity to enable and integrate consistency to optimize processes in an enterprise both within its boundaries as well as when connecting with partners, customers, and third-party services.

SUMMARY

In our approach for a data driven engineering process, we will utilize digital threads that are comprised on multi-modal data entities that are stitched together to form the flow pipeline for the digital twine. The multi-modal dataset will be composed of the 6 schemas defined in our Cybernetics Tapestry foundation whitepaper and provide the basis for digital twins and actionable intelligence. We will use the CockroachDB whose theme is scale fast, survive disaster, thrive everywhere to compose a Question Focused data mart that will feed into our Tau Codex Orchestrator.

The vision of Industry and Services 5.0 recognizes the power of industry to achieve societal goals beyond jobs and growth, to become a resilient provider of prosperity by making production respect the boundaries of the planet and place the well-being of the human at the center of the process. It emphasizes on sustainability, human-centricity, and resilience along with all the tenets that defined Industry 4.0 and the revolutions prior to it. It moves focus from solely shareholder value to stakeholder enablement, for all concerned.

The need to digitize physical assets for efficient design, development and operations led to the emergence of the digital twin concept. Creating a twin for an inorganic asset differs vastly from an organic being. We will encounter many conflicting questions and hurdles along the path. Legal, ethical, privacy and data security issues will have the societies divided on their opinions.

We at Numorpho Cybernetic Systems (NUMO) are firmly committed to providing for this next revolution of Industry 5.0 by creating an end-to-end reference architecture. Reference architectures have long been accepted as a set of recommended structures and integrations of products and services to form a solution that embodies accepted industry best practices, typically suggesting the optimal delivery method for specific technologies. They provide a frame of reference for building systems or enacting a digital transformation within an established enterprise. Using a reference architecture saves time and resources by allowing teams to avoid reinventing the wheel and to execute a plan in an efficient and economical way.

BY PI.AI

Summary of “Automate: Our DTWM™ Reference Architecture”:

Numorpho’s DTWM™ Reference Architecture is a framework designed to facilitate the integration and synchronization of systems, products, and assets across an organization. The Automate reference architecture is one of the five components of Numorpho’s Mantra M5 platform, which focuses on engineering processes for Industry 4.0 applications.

Key features of the Automate architecture include:

• A modular and scalable framework for integrating data from various sources, including databases, ERP systems, and IoT devices
• Three layers – perception, cognition, and conation – that align with cognitive processes for decision-making and action
• A modular design for the perception layer, enabling businesses to choose the best-fitting component and workflow
• Cognition layer components for real-time reasoning, optimization, and simulation
• Conation layer for seamless actuation of insights gained from the cognition layer, driving actionable results

The Digital Twine World Model™ Reference Architecture aims to help businesses create a connected and agile digital ecosystem that supports end-to-end process optimization, real-time decision-making, and improved collaboration between systems and stakeholders. This architecture is designed to adapt to unique business needs and serve as a foundation for building smart, connected products and services.

NITIN UCHIL – Founder, CEO & Technical Evangelist
nitin.uchil@numorpho.com