AILatent Space
Accelerated Understanding co-founders Anima Anandkumar and Benedikt Jenik discuss building a foundational, multi-physics model that learns physical dynamics across fluid mechanics, semiconductors, and energy systems. They detail how neural operators allow 4D rollouts with up to 5-trillion-token context windows, explain why dense physical laws enable superior self-improvement compared to language models, and reveal early commercial traction in chip design and geothermal exploration.
- Legacy chip design freezes digital logic before running physics simulations as a passive sanity check, which locks designers out of thermal and electromagnetic headroom. Read →
- Large language models struggle with sparse feedback like binary thumbs-up ratings, while physical AI models receive dense, continuous error signals across every point in space and time. Read →
- Neural operators bypass the fixed-resolution limit of standard vision models, letting teams train on coarse simulations and infer directly on dense physical meshes. Read →
- Accelerated Understanding trains physics models on 1-trillion-token inputs and outputs, scaling up to 5-trillion-token inference runs. Read →
- Training a single model across multiple physical regimes beats giving dedicated parameter budgets to narrow, domain-specific surrogate models. Read →
AILatent Space
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.
- FourCastNet delivers weather forecasts comparable to supercomputer numerical models while running tens of thousands of times faster on a single GPU. Read →
- Standard transformers hit a computational wall on high-resolution physics simulations because self-attention scales with quadratic complexity across dense 3D and 4D grids. Read →
- Caltech researcher Anima Anandkumar achieved a 1,000,000x speedup modeling tokamak plasma evolution compared to traditional physics simulations. Read →
- Classical physics-informed neural networks (PINNs) solve differential equations from scratch for each instance, often failing when equations describe turbulent or time-dependent physics. Read →
- Caltech professor Anima Anandkumar built TorchLean to connect PyTorch deep learning abstractions with the Lean formal proof assistant. Read →