DEEP LEARNING OF PARAMETERIZED EQUATIONS WITH APPLICATIONS TO UNCERTAINTY QUANTIFICATION
From MaRDI portal
Publication:5052433
DOI10.1615/Int.J.UncertaintyQuantification.2020034123zbMath1498.68283arXiv1910.07096MaRDI QIDQ5052433
Tong Qin, John D. Jakeman, Zhen Chen, Dongbin Xiu
Publication date: 24 November 2022
Published in: International Journal for Uncertainty Quantification (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1910.07096
Artificial neural networks and deep learning (68T07) Time series analysis of dynamical systems (37M10)
Related Items (12)
Adaptive experimental design for multi‐fidelity surrogate modeling of multi‐disciplinary systems ⋮ Adaptive deep density approximation for fractional Fokker-Planck equations ⋮ Solving Time Dependent Fokker-Planck Equations via Temporal Normalizing Flow ⋮ Deep-OSG: deep learning of operators in semigroup ⋮ DNN modeling of partial differential equations with incomplete data ⋮ MC-Nonlocal-PINNs: Handling Nonlocal Operators in PINNs Via Monte Carlo Sampling ⋮ NeuralUQ: A Comprehensive Library for Uncertainty Quantification in Neural Differential Equations and Operators ⋮ Energy-dissipative evolutionary deep operator neural networks ⋮ Learning the Dynamics for Unknown Hyperbolic Conservation Laws Using Deep Neural Networks ⋮ Structure-Preserving Method for Reconstructing Unknown Hamiltonian Systems From Trajectory Data ⋮ Data-Driven Learning of Nonautonomous Systems ⋮ Robust modeling of unknown dynamical systems via ensemble averaged learning
This page was built for publication: DEEP LEARNING OF PARAMETERIZED EQUATIONS WITH APPLICATIONS TO UNCERTAINTY QUANTIFICATION