FastQM rotates a candidate basis of singular vectors on the Stiefel manifold to maximize quadratic manifold approximation quality, with feature-space cost independent of full dimension, shown on turbulent airfoil-wake data.
Survey of multifidelity methods in uncertainty propagation, inference, and optimization,
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
verdicts
UNVERDICTED 7roles
background 2polarities
background 2representative citing papers
S-BOMM identifies robust solutions via cross-model consistency in optimization problems with unranked-fidelity models, backed by probabilistic bounds and empirical tests.
Introduces consensus objective aggregation for meta-optimization of scientific discovery and reports improved scaling and speedup for 3-SAT algorithm discovery using digital MemComputing machines.
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.
Reviews linear and nonlinear SciML surrogates for coupled fluid flow and transport, with new PINN modeling of turbidity currents and β-VAE mode extraction from Rayleigh-Bénard convection.
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
-
Fast Quadratic Manifold Learning For Nonlinear Dimensionality Reduction in Large-scale Systems using Riemannian Optimization
FastQM rotates a candidate basis of singular vectors on the Stiefel manifold to maximize quadratic manifold approximation quality, with feature-space cost independent of full dimension, shown on turbulent airfoil-wake data.
-
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.
-
Scientific discovery as meta-optimization: a combinatorial optimization case study
Introduces consensus objective aggregation for meta-optimization of scientific discovery and reports improved scaling and speedup for 3-SAT algorithm discovery using digital MemComputing machines.
-
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.
-
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.
-
Advances in Scientific Machine Learning for Coupled Fluid Flow and Transport
Reviews linear and nonlinear SciML surrogates for coupled fluid flow and transport, with new PINN modeling of turbidity currents and β-VAE mode extraction from Rayleigh-Bénard convection.
-
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.