Symmetry, scale, and science: A geometric path to better AI
7The success of modern AI systems has been largely driven by massive scaling of data and compute resources. However, in scientific applications, where physical constraints and geometric structures are crucial, the "scale is all you need" paradigm shows clear limitations. This talk presents cutting-edge approaches that combine the reliability of geometry-preserving neural architectures with the scalability demands of real-world scientific applications.
Through the lens of geometric deep learning, we demonstrate how incorporating symmetry and equivariance as inductive biases leads to more reliable and data-efficient AI systems. The first part explores how geometric latent representations can be learned while preserving crucial symmetries through equivariant neural fields, enabling reliable geometric reasoning and physical modeling.
The second part showcases NeuralDEM and NeuralCFD, two groundbreaking approaches that scale architectures to simulate particulate flows and automotive aerodynamics in real-time, handling systems with hundreds of thousands of particles and millions of mesh cells, respectively. While demonstrated in industrial applications, the principles behind this scalable architecture have broader implications, including potential applications in large-scale molecular dynamics simulations.
By bringing together these complementary perspectives, we demonstrate how geometric deep learning principles can deliver both reliability through geometric structure preservation and scalability through efficient architectural design.
Speakers:
Erik Bekkers
Assistant Professor, University of Amsterdam
Johannes Brandstetter
Founder and Chief Scientist, Emmi AI
Moderators:
Arnout Devos
Scientific Coordinator, European Laboratory for Learning and Intelligent Systems (ELLIS)
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The views and opinions expressed are those of the panelists and do not reflect the official policy of the ITU.