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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A probabilistic learning approach using stochastic processes predicts pre-transplant VOD severity scores from patient variables, with training data augmented via probabilistic inverse learning.
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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.
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Probabilistic learning to perform pre-onset individualised prediction of disease severity: application to Veno Occlusive Disease
A probabilistic learning approach using stochastic processes predicts pre-transplant VOD severity scores from patient variables, with training data augmented via probabilistic inverse learning.