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In this paper, we adopt conformal prediction, a distribution-free uncertainty quantification (UQ) framework, to obtain confidence prediction intervals with coverage guarantees for Deep Operator Network (DeepONet) regression.
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Yaniv Romano, Evan Patterson, and Emmanuel Candes · 2019
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Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew Stuart, and Anima Anandkumar · 2020
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George Em Karniadakis, Ioannis G Kevrekidis, Lu Lu, Paris Perdikaris, Sifan Wang, and Liu Yang · 2021
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Operator learning for predicting multiscale bubble growth dynamics
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Deepm&mnet for hypersonics: Predicting the coupled flow and finite-rate chemistry behind a normal shock using neural-network approximation of operators
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A gentle introduction to conformal prediction and distribution-free uncertainty quantification
Anastasios N Angelopoulos and Stephen Bates · 2021
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A discretization-invariant extension and analysis of some deep operator networks
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Deeponet-grid-uq: A trustworthy deep operator framework for predicting the power grid’s post-fault trajectories
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Deepgraphonet: A deep graph operator network to learn and zero-shot transfer the dynamic response of networked systems
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Zhongyi Jiang, Min Zhu, Dongzhuo Li, Qiuzi Li, Yanhua O Yuan, and Lu Lu · 2023
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Ppdonet: Deep operator networks for fast prediction of steady-state solutions in disk–planet systems
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A comprehensive and fair comparison of two neural operators (with practical extensions) based on fair data
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Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of nanoscale heat transport
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Shunyuan Mao, Ruobing Dong, Lu Lu, Kwang Moo Yi, Sifan Wang, and Paris Perdikaris · 2023
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Neural operator prediction of linear instability waves in high-speed boundary layers
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Christian Moya, Guang Lin, Tianqiao Zhao, and Meng Yue · 2023
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Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons
Apostolos F Psaros, Xuhui Meng, Zongren Zou, Ling Guo, and George Em Karniadakis · 2023
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Bayesian deep operator learning for homogenized1 to fine-scale maps for multiscale pde
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Bayesian, multifidelity operator learning for complex engineering systems-a position paper
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Victor Quach, Adam Fisch, Tal Schuster, Adam Yala, Jae Ho Sohn, Tommi S Jaakkola, and Regina Barzilay · 2023
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Fast replica exchange stochastic gradient langevin dynamics
Guanxun Li, Guang Lin, Zecheng Zhang, and Quan Zhou · 2023
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A physics-guided bi-fidelity fourier-featured operator learning framework for predicting time evolution of drag and lift coefficients
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