Beyond Surrogates: Foundational AI for Physics-Native Design

CDFAM Computational Design Symposium — Barcelona 2026 Wasil Rezk · BeyondMath A new generation of AI models is emerging — not just faster approximators, but intelligent systems that understand and generate physics. This talk explores how foundational physics AI breaks from the surrogate modeling paradigm. Unlike models that rely on customer-provided simulation data or narrow datasets, BeyondMath’s models are trained on self-generated data rooted in first principles — not interpolating outcomes, but learning the physical laws and structure of the design space itself. This approach enables something radically new: generalizable, physics-consistent predictions at near-CFD fidelity, delivered in seconds — and without the need to retrain when a geometry changes. It opens the door to simulation-native design workflows, where simulation is not a bottleneck but a continuous, integrated part of ideation and optimization. – Why surrogate AI models struggle in real-world engineering – What it means to build a foundational model that learns physics, not data correlations – Case studies from sectors like motorsport and energy – How these models enable new kinds of design tools and thinking This is not an evolution of simulation — it’s a rethinking of how AI and physics interact. Foundational AI for physics is here, and it’s reshaping the very act of designing the physical world. Links Talk page with full transcript Watch the talk on YouTube Cite this talk Search the full text of every recorded CDFAM presentation This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.designforam.com