Bayesian neural networks for uncertainty quantification in data-driven materials modeling

DOI10.1016/j.cma.2021.114079OpenAlexW3195348523MaRDI QIDQ2246265

Audrey Olivier, Michael D. Shields, L. L. Graham-Brady

Publication date: 16 November 2021

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cma.2021.114079

zbMATH Keywords

variational inferenceBayesian neural networkdata-driven materials modelingepistemic and aleatoric uncertaintiesprobabilistic model averaging

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Probabilistic models, generic numerical methods in probability and statistics (65C20)

Related Items (8)

A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data ⋮ A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems ⋮ Spiking recurrent neural networks for neuromorphic computing in nonlinear structural mechanics ⋮ Uncertainty quantification in scientific machine learning: methods, metrics, and comparisons ⋮ Projection pursuit adaptation on polynomial chaos expansions ⋮ Physics-based self-learning spiking neural network enhanced time-integration scheme for computing viscoplastic structural finite element response ⋮ On the influence of over-parameterization in manifold based surrogates and deep neural operators ⋮ Variational inference for nonlinear inverse problems via neural net kernels: comparison to Bayesian neural networks, application to topology optimization

Uses Software

• SciANN

Cites Work

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