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Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN, USA
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MuRFiV achieves unprecedented long-term prediction accuracy in spatiotemporal dynamics by merging finite-volume principles with deep learning, outperforming conventional neural networks.
Forget tedious hyperparameter sweeps; AutoSOTA automates the *entire* research pipeline, discovering 105 new SOTA models across diverse AI tasks in just five hours per paper.