Entropy-based closure for probabilistic learning on manifolds

DOI10.1016/j.jcp.2018.12.029zbMath1459.62239arXiv1803.08161OpenAlexW2962870214MaRDI QIDQ2220629

Yanyan Li

Publication date: 25 January 2021

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

Full work available at URL: https://arxiv.org/abs/1803.08161

zbMATH Keywords

probabilistic learningentropy principlestatistical learningdiffusion mapsprobability distribution on manifoldsMarkov chain Monte Carlo generator

Mathematics Subject Classification ID

Directional data; spatial statistics (62H11) Computational methods for problems pertaining to statistics (62-08) Statistics on manifolds (62R30) Monte Carlo methods (65C05) Numerical optimization and variational techniques (65K10) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (6)

Probabilistic learning and updating of a digital twin for composite material systems ⋮ Probabilistic-learning-based stochastic surrogate model from small incomplete datasets for nonlinear dynamical systems ⋮ Polynomial-chaos-based conditional statistics for probabilistic learning with heterogeneous data applied to atomic collisions of helium on graphite substrate ⋮ Sampling of Bayesian posteriors with a non-Gaussian probabilistic learning on manifolds from a small dataset ⋮ Probabilistic learning on manifolds constrained by nonlinear partial differential equations for small datasets ⋮ An ensemble synthetic eddy method for accurate treatment of inhomogeneous turbulence

Uses Software

• EGO
• PMTK

Cites Work

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