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arXiv Machine Learning By Sahana Rayan, Yash Patel, Ambuj Tewari

A Greedy PDE Router for Blending Neural Operators and Classical Methods

Read the original on arXiv Machine Learning →

A Greedy PDE Router for Blending Neural Operators and Classical Methods proposes an approximate greedy router that selects solvers from an ensemble to solve PDEs efficiently. The method addresses the challenge of lacking true error knowledge by mimicking greedy selection, aiming to combine the strengths of machine learning and classical solvers. Experiments on Poisson, convection-diffusion, and anisotropic diffusion equations demonstrate reduced time-to-solution compared to single-solver baselines and existing hybrid approaches such as HINTS.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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