Modelling of physical systems with a Hopf bifurcation using mechanistic models and machine learning

7 September 2022

Papers citing "Modelling of physical systems with a Hopf bifurcation using mechanistic models and machine learning"

11 / 11 papers shown

Title
Using scientific machine learning for experimental bifurcation analysis of dynamic systems S. Beregi David A.W. Barton D. Rezgui S. Neild AI4CE 66 20 0 22 Oct 2021
DPM: A Novel Training Method for Physics-Informed Neural Networks in Extrapolation Jungeun Kim Kookjin Lee Dongeun Lee Sheo Yon Jin Noseong Park PINN AI4CE 45 81 0 04 Dec 2020
Understanding and mitigating gradient pathologies in physics-informed neural networks Sizhuang He Yujun Teng P. Perdikaris AI4CE PINN 89 294 0 13 Jan 2020
Universal Differential Equations for Scientific Machine Learning Christopher Rackauckas Yingbo Ma Julius Martensen Collin Warner K. Zubov R. Supekar Dominic J. Skinner Ali Ramadhan Alan Edelman AI4CE 84 591 0 13 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 493 42,407 0 03 Dec 2019
Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences Motonobu Kanagawa Philipp Hennig Dino Sejdinovic Bharath K. Sriperumbudur GP BDL 126 342 0 06 Jul 2018
ResNet with one-neuron hidden layers is a Universal Approximator Hongzhou Lin Stefanie Jegelka 105 228 0 28 Jun 2018
Learning Sparse Neural Networks through $L_0$ Regularization Christos Louizos Max Welling Diederik P. Kingma 425 1,144 0 04 Dec 2017
Machine Learning of Linear Differential Equations using Gaussian Processes M. Raissi George Karniadakis 83 550 0 10 Jan 2017
An overview of gradient descent optimization algorithms Sebastian Ruder ODL 204 6,184 0 15 Sep 2016
Inferring solutions of differential equations using noisy multi-fidelity data M. Raissi P. Perdikaris George Karniadakis AI4CE 51 290 0 16 Jul 2016