Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons-Reference-Cited by-同舟云学术

Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons

Published:2023-03 Issue: Volume:477 Page:111902
ISSN:0021-9991
Container-title:Journal of Computational Physics
language:en
Short-container-title:Journal of Computational Physics

Author:

Psaros Apostolos F.,Meng Xuhui,Zou Zongren,Guo Ling,Karniadakis George Em

Funder

U.S. Department of Energy

Air Force Office of Scientific Research

Office of the Secretary of Defense

Publisher

Elsevier BV

Subject

Computer Science Applications,Physics and Astronomy (miscellaneous),Applied Mathematics,Computational Mathematics,Modeling and Simulation,Numerical Analysis

Reference227 articles.

1. Physics-informed machine learning;Karniadakis;Nat. Rev. Phys.,2021

2. Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences;Alber;npj Digit. Med.,2019

Cited by 109 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Physics-constrained polynomial chaos expansion for scientific machine learning and uncertainty quantification;Computer Methods in Applied Mechanics and Engineering;2024-11

2. A comprehensive and FAIR comparison between MLP and KAN representations for differential equations and operator networks;Computer Methods in Applied Mechanics and Engineering;2024-11

3. Common Critiques and Recommendations for Studies in Neurology Using Machine Learning Methods;Neurology;2024-10-08

4. Quantification of uncertainty in robot pose errors and calibration of reliable compensation values;Robotics and Computer-Integrated Manufacturing;2024-10

5. Clustering and uncertainty analysis to improve the machine learning-based predictions of SAFARI-1 control follower assembly axial neutron flux profiles;Annals of Nuclear Energy;2024-10