Special issue: Big data and predictive computational modeling

DOI10.1016/j.jcp.2016.03.028zbMath1349.00279OpenAlexW2322316866MaRDI QIDQ727038

Nicholas Zabaras, Phaedon-Stelios Koutsourelakis, Mark A. Girolami

Publication date: 5 December 2016

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jcp.2016.03.028

zbMATH Keywords

machine learningscientific computingbig datauncertainty quantificationpredictive modeling

Mathematics Subject Classification ID

Proceedings, conferences, collections, etc. pertaining to statistics (62-06) Proceedings of conferences of miscellaneous specific interest (00B25) Proceedings, conferences, collections, etc. pertaining to numerical analysis (65-06) Proceedings, conferences, collections, etc. pertaining to computer science (68-06)

Related Items (5)

A probabilistic generative model for semi-supervised training of coarse-grained surrogates and enforcing physical constraints through virtual observables ⋮ Incorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systems ⋮ Semi-supervised invertible neural operators for Bayesian inverse problems ⋮ A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the small data regime ⋮ Bayesian Model and Dimension Reduction for Uncertainty Propagation: Applications in Random Media

Cites Work

• Emulation of higher-order tensors in manifold Monte Carlo methods for Bayesian inverse problems
• Variational Bayesian strategies for high-dimensional, stochastic design problems
• A sparse grid based method for generative dimensionality reduction of high-dimensional data
• Development of an exchange-correlation functional with uncertainty quantification capabilities for density functional theory
• Numerical strategy for model correction using physical constraints
• Path-space variational inference for non-equilibrium coarse-grained systems
• Solving the master equation without kinetic Monte Carlo: tensor train approximations for a CO oxidation model
• Bayesian analysis of rare events
• Approximation of probability density functions by the multilevel Monte Carlo maximum entropy method
• Scalable posterior approximations for large-scale Bayesian inverse problems via likelihood-informed parameter and state reduction
• Sparse-grid, reduced-basis Bayesian inversion: nonaffine-parametric nonlinear equations

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