Beyond the Buzz: The Critical Link for Unleashing AI in CAE

Forget the buzz for a moment – whether you're an AI enthusiast or skeptic, for those of us in product development, the real question is how it translates into practical tools? I've had the chance to see the cutting-edge AI software emerging from both startups and established names in the CAE world. And I think they're all overlooking one vital ingredient that's holding AI back from its full potential. Right now, most AI tools in CAE seem to be chasing two main goals: 1. Making ML Accessible: Helping traditional CAE users tap into fundamental machine learning tools like Neural Networks, XGBoost, and Random Forests with easy-to-use interfaces. 2. Integrating Next-Gen AI: Bringing advanced algorithms and architectures (like generative CAD, Graph Neural Networks, VAE, etc.) into the familiar CAE environment. While the latter definitely sparks more excitement, I find there's still a gap preventing these tools from becoming "must-have" assets in product development. In my opinion, this stems from a continued focus on simply offering more algorithms or more robust architectures, coupled with better user interfaces. This approach mirrors how traditional CAE companies have succeeded: by providing more or better models in their software, with good customer support as a bonus. However, this isn't enough for AI-powered CAE software to truly win. The fundamental difference lies in the nature of the underlying models. A Navier-Stokes based solver with turbulence and wall models, for instance, can solve external flow problems for any given geometry (from a simple cylinder to a vehicle) because its equations are universal. This isn't the case for ML tools; they offer little value without data. The power of Large Language Models (LLMs) was truly unleashed when they were trained on massive datasets, revealing emergent behaviors only possible at that scale. Companies like OpenAI don't just provide the Transformer architecture; their true value lies in the rigorous training and post-training stages of their AI models. So, for CAE, I think companies need to offer more than just clever architectures. They need to provide a foundational model– like a pre-trained AI model specific to a certain physics (e.g., internal flow with heat transfer). This model would already "understand" the basics of that physics from extensive prior training. Then, customers could fine-tune it with their own specific in-house data (like unique geometries, materials, or fluids). That's how we truly unleash AI's potential in product design: accelerating simulation and opening the door for much faster, broader design optimizations. While the broader topic of how these tools can accelerate product development warrants a separate discussion, in a nutshell, I see a great potential for optimization. But the real secret weapon for unleashing AI in CAE will be a robust ML pipeline combined with a strong base of relevant, physics-specific data. Companies that nail the training and post-training stages will be the ones that truly stand out.