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.
arXiv preprint arXiv:2201.09113 (2022)
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Weak-DMD applies a Galerkin weak form to Dynamic Mode Decomposition to eliminate timestep constraints and filter noise in modal analysis.
A GNN-based hybrid twin learns the ignorance component of physics simulations from sparse data and generalizes corrections across meshes, geometries, and loads in nonlinear heat transfer.
PnP-Corrector decouples pre-trained physics engines from a correction agent to mitigate reciprocal error amplification in coupled spatiotemporal forecasting, cutting error by 28% on a 300-day ocean-atmosphere task.
citing papers explorer
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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.
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Weak-DMD: A Galerkin approach to the problem of noise in the Dynamic Mode Decomposition algorithm
Weak-DMD applies a Galerkin weak form to Dynamic Mode Decomposition to eliminate timestep constraints and filter noise in modal analysis.
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Bridging Data and Physics: A Graph Neural Network-Based Hybrid Twin Framework
A GNN-based hybrid twin learns the ignorance component of physics simulations from sparse data and generalizes corrections across meshes, geometries, and loads in nonlinear heat transfer.
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PnP-Corrector: A Universal Correction Framework for Coupled Spatiotemporal Forecasting
PnP-Corrector decouples pre-trained physics engines from a correction agent to mitigate reciprocal error amplification in coupled spatiotemporal forecasting, cutting error by 28% on a 300-day ocean-atmosphere task.