physical world models

Separable Neural Architecture Achieves 150,000x Speedup Over GPU Finite Element Simulations

Researchers introduce the Separable Neural Architecture (SNA), a function representational class combining neural approximation with tensor decomposition. The architecture achieves 150,000x speedup over full-grid finite element baselines on an NVIDIA A100 GPU, running Monte Carlo sweeps in 102 seconds on a standard laptop CPU. Validated on 7D parametric manufacturing and thermal inversion for Inconel 718, SNA enables real-time inverse reconstructions under 100ms.