Scalable uncertainty quantification for deep operator networks using randomized priors

DOI10.1016/j.cma.2022.115399OpenAlexW4225696772WikidataQ114196726 ScholiaQ114196726MaRDI QIDQ2674111

Georgios Kissas, Yibo Yang, Paris Perdikaris

Publication date: 22 September 2022

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

Full work available at URL: https://arxiv.org/abs/2203.03048

zbMATH Keywords

uncertainty quantificationdeep learningoperator learning

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07)

Related Items (4)

UQDeepONet ⋮ Reliable extrapolation of deep neural operators informed by physics or sparse observations ⋮ Uncertainty quantification in scientific machine learning: methods, metrics, and comparisons ⋮ Variationally mimetic operator networks

Uses Software

• Chebfun
• GAUSSIAN
• NumPy
• Matplotlib
• ImageNet
• Adam
• GitHub
• JAX
• AlexNet
• NeuralUQ
• DeepONet
• ImprovedDeepONets

Cites Work

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• Solving high-dimensional partial differential equations using deep learning
• Advanced Lectures on Machine Learning
• On kinematic waves II. A theory of traffic flow on long crowded roads

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