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NeuralUQ: A Comprehensive Library for Uncertainty Quantification in Neural Differential Equations and Operators.

, , , и . SIAM Rev., 66 (1): 161-190 (февраля 2024)

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Meng Meng

Xuhui He

Xuhui Li

Meng Meng Liang

Meng Zhang

Methods and implementations of road-network matching. TU München, (2009)
 

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NeuralUQ: A Comprehensive Library for Uncertainty Quantification in Neural Differential Equations and Operators., , , и . SIAM Rev., 66 (1): 161-190 (февраля 2024)Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems., , , и . CoRR, (2021)Uncertainty quantification for noisy inputs-outputs in physics-informed neural networks and neural operators., , и . CoRR, (2023)Variational inference in neural functional prior using normalizing flows: Application to differential equation and operator learning problems.. CoRR, (2023)PPINN: Parareal Physics-Informed Neural Network for time-dependent PDEs., , , и . CoRR, (2019)B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data., , и . J. Comput. Phys., (2021)NeuralUQ: A comprehensive library for uncertainty quantification in neural differential equations and operators., , , и . CoRR, (2022)Physics-informed neural networks for predicting gas flow dynamics and unknown parameters in diesel engines., , , и . CoRR, (2023)Uncertainty Quantification in Scientific Machine Learning: Methods, Metrics, and Comparisons., , , , и . CoRR, (2022)DeepXDE: A Deep Learning Library for Solving Differential Equations., , , и . AAAI Spring Symposium: MLPS, том 2587 из CEUR Workshop Proceedings, CEUR-WS.org, (2020)