Reproduce “Performance of Neural Operator Baselines on 'The Well' Dataset” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Yash Pincha, Andy Dimnaku, Andrew Chiang, Adarsh Ganeshram, Valentin Duruisseaux, Anima Anandkumar
The authors evaluate task-specific tuning of Tucker-factorized Fourier Neural Operators (TFNOs) on The Well benchmark. They search coarse configurations under 12-hour limits and compare one-frame predictions with published baselines that use up to four frames. Tuned TFNOs achieve lower error than both published neural-operator baselines on 15 of 17 tasks, but the comparison does not establish architectural superiority.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.