An artificial neural network approach to bifurcating phenomena in computational fluid dynamics

22 September 2021

Papers citing "An artificial neural network approach to bifurcating phenomena in computational fluid dynamics"

8 / 8 papers shown

Title
Optimal Transport-inspired Deep Learning Framework for Slow-Decaying Kolmogorov n-width Problems: Exploiting Sinkhorn Loss and Wasserstein Kernel M. Khamlich F. Pichi G. Rozza 50 4 0 26 Aug 2023
Learning normal form autoencoders for data-driven discovery of universal,parameter-dependent governing equations M. Kalia Steven L. Brunton H. Meijer C. Brune J. Nathan Kutz AI4CE 19 20 0 09 Jun 2021
Estimates on the generalization error of Physics Informed Neural Networks (PINNs) for approximating a class of inverse problems for PDEs Siddhartha Mishra Roberto Molinaro PINN 30 263 0 29 Jun 2020
Estimates on the generalization error of Physics Informed Neural Networks (PINNs) for approximating PDEs Siddhartha Mishra Roberto Molinaro PINN 45 172 0 29 Jun 2020
A comprehensive deep learning-based approach to reduced order modeling of nonlinear time-dependent parametrized PDEs S. Fresca Luca Dede' Andrea Manzoni AI4CE 44 259 0 12 Jan 2020
PyTorch: An Imperative Style, High-Performance Deep Learning Library Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury ... Sasank Chilamkurthy Benoit Steiner Lu Fang Junjie Bai Soumith Chintala ODL 292 42,038 0 03 Dec 2019
A Theoretical Analysis of Deep Neural Networks and Parametric PDEs Gitta Kutyniok P. Petersen Mones Raslan R. Schneider 53 199 0 31 Mar 2019
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 979 149,474 0 22 Dec 2014