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Can Transfer Neuroevolution Tractably Solve Your Differential Equations?

6 January 2021

Jian Cheng Wong

Abhishek Gupta

Yew-Soon Ong

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Papers citing "Can Transfer Neuroevolution Tractably Solve Your Differential Equations?"

20 / 20 papers shown

Title
Scaling MAP-Elites to Deep Neuroevolution Cédric Colas Joost Huizinga Vashisht Madhavan Jeff Clune 64 88 0 03 Mar 2020
D3M: A deep domain decomposition method for partial differential equations Ke Li Keju Tang Tianfan Wu Qifeng Liao AI4CE 62 116 0 24 Sep 2019
Machine Discovery of Partial Differential Equations from Spatiotemporal Data Ye Yuan Junlin Li Liang Li Frank Jiang Xiuchuan Tang ... J. Gonçalves H. Voss Xiuting Li J. Kurths Han Ding AI4CE 39 9 0 15 Sep 2019
$Transfer learning enhanced physics informed neural network for phase-field modeling of fracture$ Transfer learning enhanced physics informed neural network for phase-field modeling of fracture S. Goswami C. Anitescu S. Chakraborty Timon Rabczuk PINN 78 610 0 04 Jul 2019
Modeling the Dynamics of PDE Systems with Physics-Constrained Deep Auto-Regressive Networks N. Geneva N. Zabaras AI4CE 69 275 0 13 Jun 2019
Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks Georgios Kissas Yibo Yang E. Hwuang W. Witschey John A. Detre P. Perdikaris AI4CE 123 373 0 13 May 2019
AIR5: Five Pillars of Artificial Intelligence Research Yew-Soon Ong Abhishek Gupta 54 29 0 30 Dec 2018
Adversarial Uncertainty Quantification in Physics-Informed Neural Networks Yibo Yang P. Perdikaris AI4CE PINN 129 358 0 09 Nov 2018
Robust Optimization through Neuroevolution Paolo Pagliuca S. Nolfi 50 13 0 02 Oct 2018
Deep Learning of Vortex Induced Vibrations M. Raissi Zhicheng Wang M. Triantafyllou George Karniadakis AI4CE 60 376 0 26 Aug 2018
Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations M. Raissi PINN AI4CE 120 756 0 20 Jan 2018
ES Is More Than Just a Traditional Finite-Difference Approximator Joel Lehman Jay Chen Jeff Clune Kenneth O. Stanley 75 89 0 18 Dec 2017
On the Relationship Between the OpenAI Evolution Strategy and Stochastic Gradient Descent Xingwen Zhang Jeff Clune Kenneth O. Stanley 59 58 0 18 Dec 2017
A unified deep artificial neural network approach to partial differential equations in complex geometries Jens Berg K. Nystrom AI4CE 63 586 0 17 Nov 2017
The Deep Ritz method: A deep learning-based numerical algorithm for solving variational problems E. Weinan Ting Yu 123 1,389 0 30 Sep 2017
DGM: A deep learning algorithm for solving partial differential equations Justin A. Sirignano K. Spiliopoulos AI4CE 93 2,067 0 24 Aug 2017
Evolution Strategies as a Scalable Alternative to Reinforcement Learning Tim Salimans Jonathan Ho Xi Chen Szymon Sidor Ilya Sutskever 115 1,541 0 10 Mar 2017
PathNet: Evolution Channels Gradient Descent in Super Neural Networks Chrisantha Fernando Dylan Banarse Charles Blundell Yori Zwols David R Ha Andrei A. Rusu Alexander Pritzel Daan Wierstra 75 881 0 30 Jan 2017
Automatic differentiation in machine learning: a survey A. G. Baydin Barak A. Pearlmutter Alexey Radul J. Siskind PINN AI4CE ODL 168 2,816 0 20 Feb 2015
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 2.0K 150,312 0 22 Dec 2014