A framework learns boundary-to-domain pseudo-extensions to condition neural operators on complex BCs, achieving SOTA accuracy on 18 challenging PDE datasets without hyperparameter tuning.
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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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Imposing Boundary Conditions on Neural Operators via Learned Function Extensions
A framework learns boundary-to-domain pseudo-extensions to condition neural operators on complex BCs, achieving SOTA accuracy on 18 challenging PDE datasets without hyperparameter tuning.
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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.