Uncertainty quantification in scientific machine learning: methods, metrics, and comparisons (Q2681129)

scientific article

Language	Label	Description	Also known as
English	Uncertainty quantification in scientific machine learning: methods, metrics, and comparisons	scientific article

Statements

instance of

scholarly article

0 references

title

Uncertainty quantification in scientific machine learning: methods, metrics, and comparisons (English)

0 references

0 references

0 references

0 references

0 references

George Em Karniadakis

0 references

published in

Journal of Computational Physics

0 references

publication date

10 February 2023

0 references

full work available at URL

https://arxiv.org/abs/2201.07766

0 references

zbMATH Keywords

scientific machine learning

0 references

stochastic partial differential equations

0 references

uncertainty quantification

0 references

physics-informed neural networks

0 references

neural operator learning

0 references

Bayesian framework

0 references

describes a project that uses

0 references

0 references

0 references

0 references

0 references

0 references

0 references

0 references

0 references

0 references

0 references

0 references

0 references

0 references

Deep Learning: An Introduction for Applied Mathematicians

0 references

On some neural network architectures that can represent viscosity solutions of certain high dimensional Hamilton-Jacobi partial differential equations

0 references

Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

0 references

DGM: a deep learning algorithm for solving partial differential equations

0 references

fPINNs: Fractional Physics-Informed Neural Networks

0 references

Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations

0 references

PPINN: parareal physics-informed neural network for time-dependent PDEs

0 references

\textit{hp}-VPINNs: variational physics-informed neural networks with domain decomposition

0 references

On the Convergence of Physics Informed Neural Networks for Linear Second-Order Elliptic and Parabolic Type PDEs

0 references

A composite neural network that learns from multi-fidelity data: application to function approximation and inverse PDE problems

0 references

Multi-fidelity Bayesian neural networks: algorithms and applications

0 references

B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data

0 references

DeepXDE: A Deep Learning Library for Solving Differential Equations

0 references

Q3172405

0 references

Q2933843

0 references

Q4637063

0 references

Deep learning: a Bayesian perspective

0 references

Bayesian neural networks: an introduction and survey

0 references

Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods

0 references

Stochastic Gradient Markov Chain Monte Carlo

0 references

Deep UQ: learning deep neural network surrogate models for high dimensional uncertainty quantification

0 references

A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the small data regime

0 references

Conditional deep surrogate models for stochastic, high-dimensional, and multi-fidelity systems

0 references

Bayesian neural networks for uncertainty quantification in data-driven materials modeling

0 references

GAN-Based Priors for Quantifying Uncertainty in Supervised Learning

0 references

Solving inverse problems in stochastic models using deep neural networks and adversarial training

0 references

Variational Inference Formulation for a Model-Free Simulation of a Dynamical System with Unknown Parameters by a Recurrent Neural Network

0 references

Learning constitutive relations from indirect observations using deep neural networks

0 references

Simulator-free solution of high-dimensional stochastic elliptic partial differential equations using deep neural networks

0 references

Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations

0 references

Bayesian differential programming for robust systems identification under uncertainty

0 references

Wasserstein generative adversarial uncertainty quantification in physics-informed neural networks

0 references

Efficient uncertainty quantification for dynamic subsurface flow with surrogate by theory-guided neural network

0 references

GINNs: graph-informed neural networks for multiscale physics

0 references

Q3583848

0 references

Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks

0 references

Meta-learning PINN loss functions

0 references

Multivariate Chebyshev Inequality With Estimated Mean and Variance

0 references

Scalable uncertainty quantification for deep operator networks using randomized priors

0 references

Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems

0 references

Learning in Modal Space: Solving Time-Dependent Stochastic PDEs Using Physics-Informed Neural Networks

0 references

Probabilistic Forecasts, Calibration and Sharpness

0 references

Q5214292

0 references

Inconsistency of Bayesian inference for misspecified linear models, and a proposal for repairing it

0 references

Advanced Lectures on Machine Learning

0 references

Langevin diffusions and the Metropolis-adjusted Langevin algorithm

0 references

Convergence of a stochastic approximation version of the EM algorithm

0 references

An Introduction to Variational Autoencoders

0 references

Q2934059

0 references

Probable networks and plausible predictions — a review of practical Bayesian methods for supervised neural networks

0 references

Identifiers

Mathematics Subject Classification ID

0 references

0 references

0 references

0 references

0 references

10.1016/J.JCP.2022.111902

0 references

Sitelinks

Mathematics(1 entry)

mardi Publication:2681129