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Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN, USA, Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY, 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.
D-Flow SGLD unlocks scalable posterior sampling for scientific inverse problems using Flow Matching, achieving a better trade-off between measurement assimilation, posterior diversity, and physics fidelity than existing methods.
