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.
Solver-in-the-loop: Learning from differentiable physics to interact with iterative pde-solvers.Advances in neural information processing systems, 33:6111–6122
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A continuous data assimilation framework enables a-priori training of solver-conditioned neural turbulence closures that remain stable at deployment and track discretization errors.
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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.