Why FourCastNet Beats Supercomputers at Weather Forecasting
Anima Anandkumar explains how FourCastNet runs weather models tens of thousands of times faster on a single GPU by ditching flat maps for spheres.
🔬 Why Transformers Hit a Wall the Moment Physics Shows Up — Anima Anandkumar, Caltech
With Anima Anandkumar, Brandon, R.J. Honiki · Sunday, August 30, 2026
Caltech professor and former NVIDIA AI research leader Anima Anandkumar discusses why traditional deep learning and transformers fail when applied to complex physical systems due to extreme resolution requirements and scarce data. She explains how neural operators and Fourier architectures solve these bottlenecks by modeling continuous function spaces, enabling FourCastNet to deliver accurate global weather and climate forecasts thousands of times faster on a single GPU. Anandkumar also discusses formal verification with TorchLean, multiphysics foundation models for nuclear fusion and chip design, and why scientific AI requires distinct regulatory treatment from language models.
Why Transformers Fail at Physics (and Why FNOs Win)
Anima Anandkumar explains why Fourier Neural Operators beat transformers on continuous physics, running weather simulations thousands of times faster.
Inverse AI Design Beats Human Heuristics in Chip and Fusion Tech
Anima Anandkumar explains how multiphysics foundation models solve inverse design problems for fusion reactors and semiconductor masks.
Why Neural Operators Beat PINNs for Complex Physical AI
Anima Anandkumar explains why neural operators succeed where PINNs fail, bringing resolution-free modeling to physics and weather forecasting.
Anima Anandkumar on TorchLean and Verifiable AI for Physics
Anima Anandkumar introduces TorchLean, bringing formal mathematical verification in Lean to neural networks in physical control loops.