arXiv Machine Learning
Bifidelity Karhunen-Lo\`eve Expansion Surrogate with Active Learning for Random Fields
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Uncertainty Quantification of Engineering Structures by Polynomial Chaos Expansion and Multivariate Active Learning
arXiv:2606. 17233v1 Announce Type: new Abstract: In many engineering applications, a single high-fidelity model produces multiple quantities of interest (QoIs) under the same input parameters, e.
arXiv Machine Learning
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Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data
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AL-SPCE - Reliability analysis for nondeterministic models using stochastic polynomial chaos expansions and active learning
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Deep Learning of Solver-Aware Turbulence Closures from Nudged LES Dynamics
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arXiv AI
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Learning Where to Simulate: Generative Active Sampling for Online PDE Surrogate Training
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Hugging Face Trending Papers
Jun 17
Advances in Scientific Machine Learning for Coupled Fluid Flow and Transport
This chapter reviews recent advances in Scientific Machine Learning (SciML) for modeling coupled fluid flow and transport phenomena governed by the incompressible Navier-Stokes and scalar transport equations. Such systems, found in applications like turbidity currents and thermal convection, feature strong nonlinear coupling and multiscale behavior that make high-fidelity simulations computationally expensive.