Learning on dynamic statistical manifolds

DOI10.1098/rspa.2020.0213zbMath1472.62011arXiv2005.03223OpenAlexW3046001543WikidataQ98649567 ScholiaQ98649567MaRDI QIDQ5161017

F. Boso, Daniel M. Tartakovsky

Publication date: 29 October 2021

Published in: Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2005.03223

zbMATH Keywords

parameter identificationmathematical modellingBayesian inferencemethod of distributions

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Random fields; image analysis (62M40) First-order nonlinear hyperbolic equations (35L60) Topological data analysis (62R40) Information geometry (statistical aspects) (62B11)

Related Items (4)

Data-driven discovery of coarse-grained equations ⋮ Information geometry of physics-informed statistical manifolds and its use in data assimilation ⋮ Feature-informed data assimilation ⋮ Dynamics of Data-driven Ambiguity Sets for Hyperbolic Conservation Laws with Uncertain Inputs

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