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.
Surrogate modeling for fluid flows based on physics- constrained deep learning without simulation data.Computer Methods in Applied Mechanics and Engineering, 361:112732, 2020
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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.