computational-physics

PGD-NO is a neural operator that precomputes geometry decomposition to achieve linear memory scalability, enabling high-fidelity physics simulations on meshes exceeding 10 million nodes and overcoming the single-node memory bottleneck.

Introduces LAPG, a diffusion framework guided by the principle of least action to improve physical consistency during inference for out-of-distribution extrapolation tasks in physics.

New research demonstrates that silicon oscillators can solve complex computational problems exponentially faster than traditional semiconductor-based digital computers.