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Physics2Weight: Direct Neural Network Architecture and Weight Generation from Physical Laws Using Large Language Models

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 11/11/2026, 05:03:24 UTC.

Briefings

Physics2Weight (PtW) is a framework that uses large language models to directly generate neural network weights and edit architectures for solving partial differential equations, bypassing traditional gradient-based optimization. Experiments on four PDEs (heat, advection, Burgers', and Allen-Cahn) show lower solution errors compared to conventional physics-informed neural networks, though the framework reveals limitations in satisfying governing equations for nonlinear PDEs.

Key points

• Physics2Weight (PtW) generates neural network weights directly from physical laws using large language models without gradient-based optimization.
• PtW achieves lower solution errors than conventional physics-informed neural networks on four tested PDEs: heat, advection, Burgers', and Allen-Cahn equations.

Context

Recent advances in large language models have enabled weight-space programming (WSP), allowing direct neural network weight generation. Physics2Weight extends this to partial differential equations by combining physics-constrained prompting, architecture editing, and iterative weight refinement.

Limitations

Evidence covers four specific PDEs (heat, advection, Burgers', Allen-Cahn) with limited generalizability. Results show lower errors than conventional methods but reveal limitations in satisfying governing equations for nonlinear PDEs, with no further experimental data provided.

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