A physics-informed variational DeepONet for predicting the crack path in
brittle materials

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A physics-informed variational DeepONet for predicting the crack path in brittle materials

16 August 2021

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Papers citing "A physics-informed variational DeepONet for predicting the crack path in brittle materials"

18 / 18 papers shown

Title
Data-Driven Stochastic Closure Modeling via Conditional Diffusion Model and Neural Operator Xinghao Dong Chuanqi Chen Jin-Long Wu DiffM AI4CE 80 5 0 06 Aug 2024
A data-driven peridynamic continuum model for upscaling molecular dynamics Huaiqian You Yue Yu Stewart Silling M. DÉlia AI4CE 33 45 0 04 Aug 2021
Learning Functional Priors and Posteriors from Data and Physics Xuhui Meng Liu Yang Zhiping Mao J. Ferrandis George Karniadakis AI4CE 147 61 0 08 Jun 2021
Physics-informed neural networks (PINNs) for fluid mechanics: A review Shengze Cai Zhiping Mao Zhicheng Wang Minglang Yin George Karniadakis PINN AI4CE 73 1,189 0 20 May 2021
Learning the solution operator of parametric partial differential equations with physics-informed DeepOnets Sizhuang He Hanwen Wang P. Perdikaris AI4CE 97 699 0 19 Mar 2021
Data-driven learning of nonlocal models: from high-fidelity simulations to constitutive laws Huaiqian You Yue Yu Stewart Silling M. DÉlia 85 33 0 08 Dec 2020
Physics-Informed Neural Networks for Nonhomogeneous Material Identification in Elasticity Imaging Enrui Zhang Minglang Yin George Karniadakis 67 66 0 02 Sep 2020
Multipole Graph Neural Operator for Parametric Partial Differential Equations Zong-Yi Li Nikola B. Kovachki Kamyar Azizzadenesheli Burigede Liu K. Bhattacharya Andrew M. Stuart Anima Anandkumar AI4CE 214 392 0 16 Jun 2020
A nonlocal physics-informed deep learning framework using the peridynamic differential operator E. Haghighat A. Bekar E. Madenci R. Juanes PINN 35 107 0 31 May 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 514 42,449 0 03 Dec 2019
DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators Lu Lu Pengzhan Jin George Karniadakis 248 2,131 0 08 Oct 2019
An Energy Approach to the Solution of Partial Differential Equations in Computational Mechanics via Machine Learning: Concepts, Implementation and Applications E. Samaniego C. Anitescu S. Goswami Vien Minh Nguyen-Thanh Hongwei Guo Khader M. Hamdia Timon Rabczuk X. Zhuang PINN AI4CE 205 1,379 0 27 Aug 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 72 610 0 04 Jul 2019
Unsupervised Deep Learning Algorithm for PDE-based Forward and Inverse Problems Leah Bar N. Sochen 28 71 0 10 Apr 2019
Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification Yinhao Zhu N. Zabaras UQCV BDL 109 642 0 21 Jan 2018
The Deep Ritz method: A deep learning-based numerical algorithm for solving variational problems E. Weinan Ting Yu 121 1,387 0 30 Sep 2017
Solving ill-posed inverse problems using iterative deep neural networks J. Adler Ozan Oktem 83 617 0 13 Apr 2017
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 1.9K 150,115 0 22 Dec 2014

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