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arXiv Machine Learning By Muheng Li, Wenlong Mou

GAV: A scientific experimentation environment for PDE agents

Read the original on arXiv Machine Learning →

The paper introduces GAV, a scientific experimentation environment that generates verifiable PDE problems by learning a distribution of reference solutions and using the method of manufactured solutions to construct matching PDEs. GAV includes a Generator, Agent, and Verifier that together enable iterative program improvement and provide numerical feedback on accuracy and computational cost. Experiments on Navier–Stokes and Vlasov–Poisson equations show that GAV can significantly reduce test errors for existing PDE agents.

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