How AI physics has the potential to revolutionise product design

The impact of Artificial Intelligence (AI) on digital services is quickly becoming apparent. Tools like ChatGPT, Bard, and GitHub Copilot are transforming how we work and live.

What is less known is the potential for AI to revolutionise the basic nature of research, engineering, and physical product design.

Engineering and scientific computing is now the foundation of innovation. These methods often require massive computing power from supercomputing clusters (also known as high-performance computing or HPC) to run detailed simulation models that replicate the real world.

Across industries, research and development (R&D) teams use digital simulations to explore the physical world effectively. Use cases vary from inventing life-saving medicine, improving aircraft design and pioneering sustainable energy to creating self-driving vehicles, and refining manufacturing processes, among many other possibilities.

Now, AI offers the potential to supercharge engineering and scientific computing and transform how organizations innovate.

The computer simulations used today for engineering and scientific computing are increasingly benefiting with additional assistance from AI (and in some cases replaced by AI), dramatically lowering costs and helping engineers find the best answers faster.

Running simulations can be expensive, often requiring supercomputers to crunch massive data sets and execute highly complex calculations. But if you can build a machine learning (ML) model on how the physics works, you don't need to run simulations every time since your ML inference model can extrapolate the answer from the data.

This means you get the answers on how a particular design will perform faster and cheaper. Over the long term, machine learning, physics-informed neural networks, and other AI-based tools will become standard tools for all engineers and scientists to maximize productivity within R&D development.

Organizations that become adept at creating well-crafted AI-physics models will gain a solid competitive advantage. Such capabilities will help them establish a fundamental physics understanding of how different product designs will perform in real life. This has huge implications for how organizations retain knowledge in their research and product development.

AI-physics models can capture best practices knowledge about how physical objects behave – information that traditionally has been in the head of an expert, such as a scientist, engineer, or designer.

An aircraft engineer, for example, has accumulated knowledge about the best design approaches for the shape of wings, which then informs their choices regarding the types of design options they explore with digital simulations.

But that kind of information can now be captured with AI, which can then come up with a shortlist of design suggestions that engineers can then further explore with digital simulations.

Critically, suppose a company can create an ML model of best practices for wing designs (by training the AI tool on its body of knowledge about airplane wings). In that case, it can retain that expertise, regardless of if an engineer leaves the company.

This also brings far greater agility to an organization. If a company wants to build a new kind of plane that is more stable in high winds, an ML application can quickly generate the best options for the shape of the wings, helping the organization rapidly spin up new prototypes to enter new markets.

Given AI’s ability to help us understand the physical world, we are likely to see new shapes in all types of products, from buildings and aircraft to furniture and automobiles. Such innovations are being driven by another variant of AI: generative design.

Generative design works much like generative AI for writing text. By providing some basic guidance (prompts) about what you are trying to design, generative design tools will output many possible options, some of which you would not have thought up on your own.

By letting the software decide the design based on your performance objectives, some fascinating possibilities can result. Generative design, for example, is creating prototypes with a very biological look.

Organizations that embrace AI will accelerate engineering and scientific discovery while developing innovative new solutions that would be computationally prohibitive using traditional approaches.

Despite the promise of AI, organizations across industries will need to establish engineering and research best practices to help ensure they navigate this transition safely to maximize the benefits to society without needless risk.

Most importantly, AI is only as good as the information it trains on. Organizations will still need to do much work to provide the essential information to make the AI tool smart in the right ways.

Also, legal issues for AI are still very much undefined. Organizations must carefully review the outputs of AI to ensure accuracy, as well as watching for any ethical red flags.

Security is also another important consideration to make sure AI practices don’t accidentally expose intellectual property or proprietary information.

Certainly, guardrails for how organizations use AI are essential as we work through the early days of this new technology. But with some thoughtful measures in place, AI can safely open up all new possibilities for research and development, helping organizations move faster, become more agile, and discover better ways to invent the future.

Supporting the adoption of AI physics will help us make better products faster, accelerate the R&D innovation process, and explore the boundaries of knowledge to develop new engineering breakthroughs and scientific discoveries.