PACE-FNO reduces OOD relative error by up to 12x versus FNO with symmetry augmentation on Burgers, shallow-water, and Navier-Stokes equations by jointly training a frame estimator and operator under bounded symmetry perturbations.
Pope.Turbulent Flows
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Wavelet Flow Matching emulates multi-scale PDE-governed systems by transporting velocities directly in a hierarchical wavelet representation via U-Net, yielding improved long-horizon stability and spectral accuracy on fluid benchmarks.
Distilled one-step consistency model from optimal-transport flow-matching teacher reconstructs high-fidelity dynamical system flows from low-fidelity data with 12x speedup, half the parameters, and 23.1% better SSIM than scratch-trained baselines.
citing papers explorer
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Physics-Aligned Canonical Equivariant Fourier Neural Operator under Symmetry-Induced Shifts
PACE-FNO reduces OOD relative error by up to 12x versus FNO with symmetry augmentation on Burgers, shallow-water, and Navier-Stokes equations by jointly training a frame estimator and operator under bounded symmetry perturbations.
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Wavelet Flow Matching for Multi-Scale Physics Emulation
Wavelet Flow Matching emulates multi-scale PDE-governed systems by transporting velocities directly in a hierarchical wavelet representation via U-Net, yielding improved long-horizon stability and spectral accuracy on fluid benchmarks.
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Physical Fidelity Reconstruction via Improved Consistency-Distilled Flow Matching for Dynamical Systems
Distilled one-step consistency model from optimal-transport flow-matching teacher reconstructs high-fidelity dynamical system flows from low-fidelity data with 12x speedup, half the parameters, and 23.1% better SSIM than scratch-trained baselines.