SLE-FNO achieves zero forgetting and strong plasticity-stability balance in continual learning for FNO surrogate models of pulsatile blood flow by adding minimal single-layer extensions across four out-of-distribution tasks.
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A roadmap is outlined for digital twins in coronary artery disease that combine mathematical models with patient data through assimilation and probabilistic models to estimate wall shear stress and support clinical decisions for preventing infarcts.
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SLE-FNO: Single-Layer Extensions for Task-Agnostic Continual Learning in Fourier Neural Operators
SLE-FNO achieves zero forgetting and strong plasticity-stability balance in continual learning for FNO surrogate models of pulsatile blood flow by adding minimal single-layer extensions across four out-of-distribution tasks.
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Digital Twins in Coronary Artery Disease: A Mathematical Roadmap
A roadmap is outlined for digital twins in coronary artery disease that combine mathematical models with patient data through assimilation and probabilistic models to estimate wall shear stress and support clinical decisions for preventing infarcts.