Deep learning observables in computational fluid dynamics

7 March 2019

Papers citing "Deep learning observables in computational fluid dynamics"

27 / 27 papers shown

Title
A general physics-constrained method for the modelling of equation's closure terms with sparse data Tian Chen Shengping Liu Li Liu Heng Yong PINN AI4CE 46 0 0 30 Apr 2025
$A numerical approach for the fractional Laplacian via deep neural networks$ A numerical approach for the fractional Laplacian via deep neural networks Nicolás Valenzuela 34 3 0 30 Aug 2023
A Survey on Solving and Discovering Differential Equations Using Deep Neural Networks Hyeonjung Jung Jung Jayant Gupta B. Jayaprakash Matthew J. Eagon Harish Selvam Carl Molnar W. Northrop Shashi Shekhar AI4CE 35 5 0 26 Apr 2023
Efficient Quality Diversity Optimization of 3D Buildings through 2D Pre-optimization Alexander Hagg Martin L. Kliemank A. Asteroth D. Wilde M. Bedrunka H. Foysi D. Reith AI4CE 20 1 0 28 Mar 2023
Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles R. Maulik Romain Egele Krishnan Raghavan Prasanna Balaprakash UQCV AI4TS AI4CE 30 6 0 20 Feb 2023
Multi-Scale Message Passing Neural PDE Solvers Léonard Equer T. Konstantin Rusch Siddhartha Mishra AI4CE 38 12 0 07 Feb 2023
Nonlinear Reconstruction for Operator Learning of PDEs with Discontinuities S. Lanthaler Roberto Molinaro Patrik Hadorn Siddhartha Mishra 56 24 0 03 Oct 2022
NeuralUQ: A comprehensive library for uncertainty quantification in neural differential equations and operators Zongren Zou Xuhui Meng Apostolos F. Psaros George Karniadakis AI4CE 32 36 0 25 Aug 2022
Error analysis for deep neural network approximations of parametric hyperbolic conservation laws Tim De Ryck Siddhartha Mishra PINN 15 10 0 15 Jul 2022
A comparison of PINN approaches for drift-diffusion equations on metric graphs J. Blechschmidt Jan-Frederik Pietschman Tom-Christian Riemer Martin Stoll M. Winkler 18 2 0 15 May 2022
Error estimates for physics informed neural networks approximating the Navier-Stokes equations Tim De Ryck Ameya Dilip Jagtap S. Mishra PINN 49 115 0 17 Mar 2022
Cell-average based neural network method for hyperbolic and parabolic partial differential equations Changxin Qiu Jue Yan 16 10 0 02 Jul 2021
Error analysis for physics informed neural networks (PINNs) approximating Kolmogorov PDEs Tim De Ryck Siddhartha Mishra PINN 21 100 0 28 Jun 2021
Physics perception in sloshing scenes with guaranteed thermodynamic consistency B. Moya Alberto Badías D. González Francisco Chinesta Elías Cueto 35 14 0 24 Jun 2021
Real-time simulation of parameter-dependent fluid flows through deep learning-based reduced order models S. Fresca Andrea Manzoni AI4CE 21 36 0 10 Jun 2021
Neural network architectures using min-plus algebra for solving certain high dimensional optimal control problems and Hamilton-Jacobi PDEs Jérome Darbon P. Dower Tingwei Meng 8 22 0 07 May 2021
On the approximation of functions by tanh neural networks Tim De Ryck S. Lanthaler Siddhartha Mishra 23 138 0 18 Apr 2021
An overview on deep learning-based approximation methods for partial differential equations C. Beck Martin Hutzenthaler Arnulf Jentzen Benno Kuckuck 30 146 0 22 Dec 2020
Higher-order Quasi-Monte Carlo Training of Deep Neural Networks M. Longo Suman Mishra T. Konstantin Rusch Christoph Schwab 35 20 0 06 Sep 2020
Deep neural network approximation for high-dimensional elliptic PDEs with boundary conditions Philipp Grohs L. Herrmann 30 52 0 10 Jul 2020
Estimates on the generalization error of Physics Informed Neural Networks (PINNs) for approximating PDEs Siddhartha Mishra Roberto Molinaro PINN 25 171 0 29 Jun 2020
Space-time deep neural network approximations for high-dimensional partial differential equations F. Hornung Arnulf Jentzen Diyora Salimova AI4CE 24 19 0 03 Jun 2020
Enhancing accuracy of deep learning algorithms by training with low-discrepancy sequences Siddhartha Mishra T. Konstantin Rusch 24 49 0 26 May 2020
Uniform error estimates for artificial neural network approximations for heat equations Lukas Gonon Philipp Grohs Arnulf Jentzen David Kofler David Siska 29 34 0 20 Nov 2019
A Multi-level procedure for enhancing accuracy of machine learning algorithms K. Lye Siddhartha Mishra Roberto Molinaro 17 32 0 20 Sep 2019
Space-time error estimates for deep neural network approximations for differential equations Philipp Grohs F. Hornung Arnulf Jentzen Philipp Zimmermann 26 33 0 11 Aug 2019
Deep splitting method for parabolic PDEs C. Beck S. Becker Patrick Cheridito Arnulf Jentzen Ariel Neufeld 23 125 0 08 Jul 2019