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Graph Neural Simulators (GNS) is proposed as a principled surrogate modeling paradigm for time-dependent PDEs that mimics traditional numerical solvers, enabling stable long-horizon rollouts and strong inductive biases that enhance generalization.
A three-stage framework is introduced that freezes the spatial basis of a physics-informed NO, extrapolates its solution branch to an out-of-distribution parameter using a polynomial continuation prior, and distills the resulting field into a fresh PINN, establishing a simple principle: an NO need not accurately predic...
This work presents AutoMat, a benchmark for evaluating LLM-based agents'ability to reproduce claims from computational materials science, and curates a set of claims from real materials science papers to test whether coding agents can recover and execute the end-to-end workflow needed to support (or undermine) such cla...
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