Deprecated: $wgMWOAuthSharedUserIDs=false is deprecated, set $wgMWOAuthSharedUserIDs=true, $wgMWOAuthSharedUserSource='local' instead [Called from MediaWiki\HookContainer\HookContainer::run in /var/www/html/w/includes/HookContainer/HookContainer.php at line 135] in /var/www/html/w/includes/Debug/MWDebug.php on line 372
scientific article; zbMATH DE number 7626805 - MaRDI portal

scientific article; zbMATH DE number 7626805

From MaRDI portal

MaRDI QIDQ5053337

Nikola B. Kovachki, Siddhartha Mishra, Samuel Lanthaler

Publication date: 6 December 2022

Full work available at URL: https://arxiv.org/abs/2107.07562

Title: zbMATH Open Web Interface contents unavailable due to conflicting licenses.



Related Items

MIONet: Learning Multiple-Input Operators via Tensor Product, Learning deep implicit Fourier neural operators (IFNOs) with applications to heterogeneous material modeling, On Bayesian data assimilation for PDEs with ill-posed forward problems, Deep learning methods for partial differential equations and related parameter identification problems, Mesh-informed neural networks for operator learning in finite element spaces, Data-driven soliton mappings for integrable fractional nonlinear wave equations via deep learning with Fourier neural operator, DNN modeling of partial differential equations with incomplete data, Reliable extrapolation of deep neural operators informed by physics or sparse observations, Exponential Convergence of Deep Operator Networks for Elliptic Partial Differential Equations, Convergence Rates for Learning Linear Operators from Noisy Data, Tutorial on Amortized Optimization, Quality measures for the evaluation of machine learning architectures on the quantification of epistemic and aleatoric uncertainties in complex dynamical systems, Bi-fidelity modeling of uncertain and partially unknown systems using DeepONets, An introduction to kernel and operator learning methods for homogenization by self-consistent clustering analysis, Local approximation of operators, Variationally mimetic operator networks, Neural Control of Parametric Solutions for High-Dimensional Evolution PDEs, Designing universal causal deep learning models: The geometric (Hyper)transformer, Optimal Dirichlet boundary control by Fourier neural operators applied to nonlinear optics, Greedy training algorithms for neural networks and applications to PDEs, Approximation bounds for convolutional neural networks in operator learning, Solving parametric partial differential equations with deep rectified quadratic unit neural networks


Uses Software


Cites Work