Bayesian differential programming for robust systems identification under uncertainty

DOI10.1098/rspa.2020.0290zbMath1472.68166arXiv2004.06843OpenAlexW3107973042WikidataQ104574373 ScholiaQ104574373MaRDI QIDQ5161165

Paris Perdikaris, Yibo Yang, Mohamed Aziz Bhouri

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/2004.06843

zbMATH Keywords

dynamical systemscomputational mathematicsmachine learninguncertainty quantificationapplied mathematicscomputer modelling and simulationmodel discovery

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) System identification (93B30) Mathematical modeling or simulation for problems pertaining to systems and control theory (93-10)

Related Items (8)

Uncertainty quantification in scientific machine learning: methods, metrics, and comparisons ⋮ A Review of Data‐Driven Discovery for Dynamic Systems ⋮ Operator inference with roll outs for learning reduced models from scarce and low-quality data ⋮ Scalable Bayesian optimization with randomized prior networks ⋮ NeuralUQ: A Comprehensive Library for Uncertainty Quantification in Neural Differential Equations and Operators ⋮ Physics-integrated neural differentiable (PiNDiff) model for composites manufacturing ⋮ On the eigenvector bias of Fourier feature networks: from regression to solving multi-scale PDEs with physics-informed neural networks ⋮ A Bayesian approach for data-driven dynamic equation discovery

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