LFNO is a dual-branch neural operator combining Laplace and Fourier methods to explicitly decompose and model transient and steady-state dynamics, outperforming baselines on ODE benchmarks and remaining competitive on PDEs.
arXiv preprint arXiv:2504.20249 , year=
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Physics-informed neural operators accurately reproduce cardiac electrophysiology dynamics over long horizons, generalize to unseen conditions and higher resolutions, and run faster than traditional numerical solvers.
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LFNO: Bridging Laplace and Fourier via Transient-Steady Decomposition
LFNO is a dual-branch neural operator combining Laplace and Fourier methods to explicitly decompose and model transient and steady-state dynamics, outperforming baselines on ODE benchmarks and remaining competitive on PDEs.
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Physics-Informed Neural Operators for Cardiac Electrophysiology
Physics-informed neural operators accurately reproduce cardiac electrophysiology dynamics over long horizons, generalize to unseen conditions and higher resolutions, and run faster than traditional numerical solvers.