MuRFiV combines multi-resolution deep learning with finite-volume inductive bias to deliver stable, accurate long-term autoregressive predictions of spatiotemporal PDE dynamics, outperforming standard neural baselines.
Ma- chine learning–accelerated computational fluid dynamics.Proceedings of the National Academy of Sciences, 118(21):e2101784118, 2021
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CE 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
A Multi-Resolution Finite-Volume Inspired Deep Learning Framework for Spatiotemporal Dynamics Prediction
MuRFiV combines multi-resolution deep learning with finite-volume inductive bias to deliver stable, accurate long-term autoregressive predictions of spatiotemporal PDE dynamics, outperforming standard neural baselines.