S-BOMM identifies robust solutions via cross-model consistency in optimization problems with unranked-fidelity models, backed by probabilistic bounds and empirical tests.
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Two new DOD-based reduced-order models (DOD-DL-ROM and DOD+DFNN) are introduced for hybrid-type parabolic PDEs, with rigorous error bounds linking performance to optimal map regularity and conditions for outperforming POD methods.
A probabilistic ROM framework calibrates correction factors for a generalized one-fiber model using Bayesian inference on full-order isogeometric cardiac data and uses Gaussian processes for online prediction with uncertainty quantification.
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.
citing papers explorer
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A Consistency-Centric Approach to Set-Based Optimization with Multiple Models of Unranked Fidelity
S-BOMM identifies robust solutions via cross-model consistency in optimization problems with unranked-fidelity models, backed by probabilistic bounds and empirical tests.
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A New Adaptive Deep Learning based Reduced Order Model for Hybrid-Type Parabolic PDEs: Rigorous Error Analysis and Applications
Two new DOD-based reduced-order models (DOD-DL-ROM and DOD+DFNN) are introduced for hybrid-type parabolic PDEs, with rigorous error bounds linking performance to optimal map regularity and conditions for outperforming POD methods.
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A probabilistic reduced-order modeling framework for patient-specific cardio-mechanical analysis
A probabilistic ROM framework calibrates correction factors for a generalized one-fiber model using Bayesian inference on full-order isogeometric cardiac data and uses Gaussian processes for online prediction with uncertainty quantification.
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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.