Separable Neural Architectures as Physical World Models: from Mathematical Theory to Applications

In three linesNew Separable Neural Architecture (SNA) combining neural approximation with tensor decomposition to solve high-dimensional PDEs. Variational framework (VSNA) guarantees well-posedness and convergence. Demonstrates 150,000x speedup vs FEM on A100 GPU for 7D parametric simulation and real-time thermal inversion of Inconel 718 (<100ms).

Summary generated by Claude — human-verified