OVERVIEW

This document approaches engineering from a parametric modeling underpinning to taking an approach of understanding the end result and incorporating it in the elements of design.We call this Outcome Based Adaptive Engineering (OBAE), and it combines:

• design thinking,
• parametric modeling,
• generative design,
• systems engineering, and
• additive manufacturing 

OBAE is based on the concepts of Objectives and Key Results (OKR), a goal-setting framework used by many companies and organizations to set clear, measurable, and achievable objectives.

The idea is to set high-level objectives that guide the overall direction of the solution, and then break them down into specific, measurable key results that are aligned with those objectives. The main idea behind OBAE is to provide clear direction, motivate and engage teams, define an agile approach for progression and hold everyone accountable for achieving the goals. It’s like a roadmap for success, with regular check-ins to make sure everyone is on track.

It is the prime basis for Numorpho Cybernetic Systems (NUMO) smart products and services play and is the core of our Mantra M5 (Make+Manage+Move+Market+Maintain) platform for process automation.

We plan to use the Wolfram Language to provide for the engineering design and analysis for the OBAE. It will also provide the basis for an intelligent inference engine for our TAU Codex Transformer that will be trained to automatically design such products in the future using a combination of:

• themed LLM (Large language model) learning,
• physics and mathematics
• multimodal data, and
• the utilization of images and patterns.

TABLE OF CONTENTS

• Overview
• A Beautiful Mess (Blog and Podcast)
• Computer Aided Engineering
• Outcome Basis
• The Wolfram Language
• Parametric Modeling
• Generative Design
• Actionable Intelligence
• ChatGPT plugin
• Summary

OVERVIEW

Design Innovation is about generating ideas that are desirable, technologically feasible, and financially viable. It is an idea that applies to all fields and people of all backgrounds.

In the endeavor towards Design Innovation, we have to define what we refer to with the term Design with a capital D. Design refers to building human understanding to solve a problem. It is a reference to the relation between things and humans. With this understanding, the outcome of Design varies with the variance in human cultures and lifestyles. In different contexts, design can refer to the hard design practices like product design, engineering design, graphic design, fashion design, and others.

Design Innovation has been growing as its own field for the last half-century. With that came the development of many supporting tools, methodologies, and schools of thought. This growth gave birth to individuals who are more specialized in the processes of Design Innovation.

However, behind all those is the mindset of caring for human needs as much as business ones. Specialized individuals can play a crucial role in facilitating and guiding the process of design innovation within organizations. This is why specialized Design Innovation firms can lead the generating innovations or insights outside the organization.

Many different methods are used to enable Design Innovation. These include Design Thinking, Open Innovation, Innovation Tournaments, Biomimicry, Frame Creation, Design by Decomposition, etc. At NUMO, we will encompass all of the methods to develop a themed curriculum for design innovation that is part of our MANTHAN Innovation Philosophy.

References:

• school.tfordesign.com

A BEAUTIFUL MESS (BLOG AND PODCAST)

The blog post Keep Product Management messy by John Cutler discusses the importance of embracing the messy and unpredictable nature of product management, arguing that it fosters innovation and better solutions. Here is a summary of the key points:

1. Traditional product management, rooted in industrial processes, can stifle creativity and innovation due to its linear and systematic approach.

2. Embracing the messy and unpredictable aspects of product management allows for iterative problem-solving and leads to more innovative solutions.

3. Learning to live with uncertainty is critical, as it pushes teams to challenge assumptions and explore new ideas.

4. Focusing on outcomes rather than specific roadmaps allows product teams to adapt quickly to changing circumstances and customer needs.

5. Using roadmaps as communication tools rather than strict plans allows for flexibility and encourages collaboration between teams.

The post emphasizes that product management should be viewed as an ongoing process of discovery, where iteration and adaptability are crucial for success. By embracing uncertainty and encouraging experimentation, product teams can better address complex problems and deliver more value to customers.

COMPUTER BASED ENGINEERING

Engineering simulation, which initially emerged in the Aerospace industry, has been the powerhouse for some traditional engineering software providers who have predominantly focused on singular approaches such as Finite Element Analysis (FEA) for structural analysis or Computational Fluid Dynamics (CFD) for fluid flow simulations. In recent times, these providers have expanded their offerings by incorporating multi-physics models and extending their capabilities to domains such as Electronics, Magnetism, Acoustics, and other scientific disciplines.

The advent of GPUs (Graphics Processing Units) has had a significant impact on the landscape of engineering simulation. Previously, software development and its computational aspects heavily relied on writing code and algorithms. However, with the rise of GPUs, hardware has taken center stage in accelerating simulations and computations. This shift has enabled functionalities like ray-tracing and graphics rendering to be available as hardware features, streamlining the simulation process and enhancing visualizations without the need for extensive code development.

This evolution in hardware capabilities has brought about a transformation in engineering simulation, empowering engineers and researchers to leverage more advanced visualization techniques and rendering capabilities. By offloading computationally intensive tasks to specialized hardware, engineers can focus on the actual analysis and interpretation of simulation results, improving overall productivity and efficiency.

The expansion of engineering simulation beyond traditional domains, along with the integration of powerful hardware features, has opened up new possibilities for solving complex engineering problems across various disciplines. This trend highlights the increasing importance of a multidisciplinary and integrated approach in engineering software development, enabling engineers to simulate and analyze the behavior of complex systems more accurately and comprehensively.

OUTCOME BASIS

Outcome Based Adaptive Engineering (OBAE) is a combination of:

• Parametric Modeling,
• Generative Design and
• Actionable intelligence

utilizing design techniques, simulation and AI/ML to build the next generation of smart and connected products. We plan to use ChatGPT plugged in with the Wolfram Language to enable engineering design, analysis and manufacturing so that the product can be created and understood in unison end to end as it progresses from design to production and beyond.

THE WOLFRAM LANGUAGE

The Wolfram Language is a highly developed knowledge-based language that unifies a broad range of programming paradigms and uses its unique concept of symbolic programming to add a new level of flexibility to the very concept of programming. It is a symbolic language, deliberately designed with the breadth and unity needed to develop powerful programs quickly.

By integrating high-level forms: Image, GeoPolygon or Molecule – along with advanced superfunctions – such as ImageIdentify or ApplyReaction – Wolfram Language makes it possible to quickly express complex ideas in computational form.

The philosophy of Wolfram Language is to build as much knowledge – about algorithms and the world – into the language as possible. Here are some of its characteristics that we plan to utilize for OBAE:

Key references:

• https://www.wolfram.com/wolfram-u/courses/wolfram-language/an-elementary-introduction-to-the-wolfram-language/
• https://reference.wolfram.com/language/
• https://www.wolfram.com/language/core-areas/geometry/
• https://www.wolfram.com/language/core-areas/fem/
• https://mathworld.wolfram.com/

Wolfram Language and Mathematica

The Wolfram Language and Mathematica are closely related, but they have some distinctions:

1. Definition:

• The Wolfram Language: It is a general-purpose programming language developed by Wolfram Research. It powers the computational engine behind various Wolfram products, including Mathematica.
• Mathematica: It is a software application developed by Wolfram Research that provides a comprehensive environment for technical computing. Mathematica includes the Wolfram Language as its programming language.

2. Scope and Functionality:

• The Wolfram Language: It is a complete programming language with a wide range of built-in computational and symbolic capabilities. It allows you to perform tasks such as mathematical calculations, data analysis, image processing, machine learning, visualization, and more.
• Mathematica: It is an interactive technical computing environment that utilizes the Wolfram Language. Mathematica includes the Wolfram Language’s features and extends them with specialized functions and tools for various areas of technical computing, such as mathematics, physics, engineering, finance, and more.

3. Availability:

• The Wolfram Language: It is available as a standalone language and can be used in various programming environments and frameworks, both within and outside the Mathematica ecosystem. It is also integrated into other Wolfram products, such as Wolfram|Alpha and the Wolfram Cloud, and more recently as a plug-in into ChatGPT.
• Mathematica: It is a commercial software application that provides a complete integrated environment for technical computing. Mathematica includes the Wolfram Language and additional specialized functionality. Mathematica licenses are available for purchase from Wolfram Research.

4. Usage and Context:

• The Wolfram Language: It is used not only within Mathematica but also as an independent language for computational tasks, algorithm development, prototyping, data analysis, automation, and other purposes. It can be used in command-line environments, notebooks, scripts, or integrated with other programming languages.
• Mathematica: It is primarily used within the Mathematica environment for technical and scientific computations, symbolic mathematics, data visualization, interactive exploration, and publication-quality document creation.

In summary, the Wolfram Language is the general-purpose programming language developed by Wolfram Research, while Mathematica is a comprehensive technical computing environment that incorporates the Wolfram Language as its programming language. The Wolfram Language can be used independently, while Mathematica provides an integrated environment with additional specialized functionality.

CHATGPT PLUG IN

The Wolfram plugin makes ChatGPT smarter by giving it access to powerful computation, accurate math, curated knowledge, real-time data and visualization through Wolfram|Alpha and Wolfram Language.

https://www.wolfram.com/wolfram-plugin-chatgpt/

From their announcement:

“One particularly significant thing here is that ChatGPT isn’t just using us to do a “dead-end” operation like show the content of a webpage. Rather, we’re acting much more like a true “brain implant” for ChatGPT—where it asks us things whenever it needs to, and we give responses that it can weave back into whatever it’s doing. It’s rather impressive to see in action. And—although there’s definitely much more polishing to be done—what’s already there goes a long way towards (among other things) giving ChatGPT the ability to deliver accurate, curated knowledge and data—as well as correct, nontrivial computations.

But there’s more too. We already saw examples where we were able to provide custom-created visualizations to ChatGPT. And with our computation capabilities we’re routinely able to make “truly original” content—computations that have simply never been done before. And there’s something else: while “pure ChatGPT” is restricted to things it “learned during its training”, by calling us it can get up-to-the-moment data.”

Here’s a systematic rundown of how we will utilize ChatGPT, the Wolfram Language, and a scientific/mathematical basis to enable end-to-end engineering design, analysis, and manufacturing for OBAE:

1. Define the Problem and Objectives: Clearly define the problem statement and the objectives of your engineering project. This could be designing a specific product, optimizing a system, or improving an existing design.

2. Gather Requirements and Constraints: Identify the requirements and constraints for your project. These can include functional requirements, performance criteria, manufacturing limitations, cost constraints, and any other relevant factors.

3. Parametric Modeling with ChatGPT: Use ChatGPT to interactively define and modify the parameters of your design. ChatGPT can assist you in generating 3D CAD models based on user inputs, providing design suggestions, and incorporating design changes in real-time.

4. Generative Design: Employ generative design techniques to automatically explore a wide range of design possibilities. Utilize AI/ML algorithms to optimize the design based on various parameters, such as weight, strength, cost, or other specified objectives.

5. Simulation and Analysis: Utilize the capabilities of the Wolfram Language to perform simulation and analysis on your design. The Wolfram Language offers powerful numerical and symbolic computation, enabling you to analyze structural integrity, fluid dynamics, thermal properties, and other engineering aspects.

6. Integration of Design and Manufacturing: Ensure seamless integration between the design and manufacturing stages. The Wolfram Language can help generate toolpaths, optimize manufacturing processes, and provide insights into material selection and fabrication techniques.

7. Actionable Intelligence and Decision-Making: Leverage the AI/ML capabilities of ChatGPT and the Wolfram Language to provide actionable intelligence. Use historical data, simulations, and real-time analysis to make informed decisions throughout the design, analysis, and manufacturing process.

8. Iterative Refinement: Engage in an iterative process of design, analysis, and manufacturing refinement based on the insights and feedback obtained. ChatGPT and the Wolfram Language can help facilitate this process by allowing you to iterate and optimize your design quickly.

9. Validation and Testing: Validate your design through physical testing or virtual prototyping. Utilize simulation tools in the Wolfram Language to validate the design’s performance against the defined requirements and constraints.

10. Documentation and Collaboration: Document your design process, analysis results, and manufacturing specifications using the capabilities of the Wolfram Language. Collaborate with other team members by sharing code, data, and visualizations to enhance communication and foster teamwork.

SUMMARY

By combining the interactive capabilities of ChatGPT, the computational power of the Wolfram Language, and a solid foundation in science and math, we will create a systematic workflow for end-to-end engineering design, analysis, and manufacturing. This approach will enable continuous improvement and optimization throughout the entire product development lifecycle.

This matches with our contention for data engineering wherein it is a combination of training and physmatic (physics + math) based learning and real time analysis that would help prescribe the outcome. Our Mantra M5 Platform would coordinate Make, Manage, Move, Market and Maintain to drive actionable intelligence at every step of the process – upstream, midstream and downstream.

Insights from the “Keep Product Management Messy” analysis account for the inherent uncertainty and unpredictability in engineering and design processes. By fostering an adaptive, outcome-oriented mindset, encourages teams to embrace uncertainty, iterate on solutions, and focus on collaboration.

Numorpho Cybernetic Systems (NUMO)‘s Outcome Based Adaptive Engineering (OBAE) is an innovative approach that combines traditional Model Based Systems Engineering (MBSE) with adaptive, objective-focused methodologies to enhance the design and engineering process. This approach aligns well with the idea of maintaining a flexible, iterative, and “messy” but accountable approach to product management, ensuring that teams can continuously refine their designs and processes to deliver optimal results.

Here is a summary of key points:

1. OBAE builds upon MBSE by incorporating feedback loops and adaptive mechanisms to respond to evolving requirements, constraints, and system behavior.
2. MBSE provides a structured, model-driven approach to complex system design, while OBAE adds the ability to adapt and optimize based on real-time insights and desired outcomes.
3. The three principles of OBAE are: Continuous Learning, Collaborative Problem-Solving, and Performance-Driven Adaptation.
4. OBAE enables product development teams to navigate uncertainty, manage complexity, and achieve desired outcomes by emphasizing iterative learning and adaptation.

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