Learning physics-based models from data: perspectives from inverse problems and model reduction

From MaRDI portal
Publication:5887831

DOI10.1017/S0962492921000064OpenAlexW3194724611MaRDI QIDQ5887831

Karen Willcox, Omar Ghattas

Publication date: 14 April 2023

Published in: Acta Numerica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1017/s0962492921000064



Related Items

An Offline-Online Decomposition Method for Efficient Linear Bayesian Goal-Oriented Optimal Experimental Design: Application to Optimal Sensor Placement, Variational Bayesian approximation of inverse problems using sparse precision matrices, Canonical and noncanonical Hamiltonian operator inference, Bayesian operator inference for data-driven reduced-order modeling, Learning high-dimensional parametric maps via reduced basis adaptive residual networks, Operator inference for non-intrusive model reduction with quadratic manifolds, Fully probabilistic deep models for forward and inverse problems in parametric PDEs, Adaptive learning of effective dynamics for online modeling of complex systems, Hierarchical model reduction driven by machine learning for parametric advection-diffusion-reaction problems in the presence of noisy data, Bayesian model calibration for diblock copolymer thin film self-assembly using power spectrum of microscopy data and machine learning surrogate, Residual-based error correction for neural operator accelerated Infinite-dimensional Bayesian inverse problems, CUQIpy: I. Computational uncertainty quantification for inverse problems in Python, CUQIpy: II. Computational uncertainty quantification for PDE-based inverse problems in Python, Large-scale Bayesian optimal experimental design with derivative-informed projected neural network, Nonintrusive Reduced-Order Models for Parametric Partial Differential Equations via Data-Driven Operator Inference, Residual-based error corrector operator to enhance accuracy and reliability of neural operator surrogates of nonlinear variational boundary-value problems, Stochastic PDE representation of random fields for large-scale Gaussian process regression and statistical finite element analysis, DNN-MG: a hybrid neural network/finite element method with applications to 3D simulations of the Navier-Stokes equations, Derivative-informed neural operator: an efficient framework for high-dimensional parametric derivative learning, Data assimilation -- mathematical foundation and applications. Abstracts from the workshop held February 20--26, 2022, Data-driven modeling of linear dynamical systems with quadratic output in the AAA framework


Uses Software


Cites Work