An End-to-End Deep Learning Method for Solving Nonlocal Allen-Cahn and Cahn-Hilliard Phase-Field Models

11 October 2024

Papers citing "An End-to-End Deep Learning Method for Solving Nonlocal Allen-Cahn and Cahn-Hilliard Phase-Field Models"

5 / 5 papers shown

Title
Physics-Informed Deep Neural Operator Networks S. Goswami Aniruddha Bora Yue Yu George Karniadakis PINN AI4CE 92 108 0 08 Jul 2022
Learning two-phase microstructure evolution using neural operators and autoencoder architectures Vivek Oommen K. Shukla S. Goswami Rémi Dingreville George Karniadakis AI4CE 105 124 0 11 Apr 2022
Solving Allen-Cahn and Cahn-Hilliard Equations using the Adaptive Physics Informed Neural Networks Colby Wight Jia Zhao 77 225 0 09 Jul 2020
DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators Lu Lu Pengzhan Jin George Karniadakis 248 2,162 0 08 Oct 2019
Cahn--Hilliard inpainting with the double obstacle potential H. Garcke K. F. Lam V. Styles 25 19 0 17 Jan 2018