Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of nanoscale heat transport

14 April 2022

Papers citing "Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of nanoscale heat transport"

7 / 7 papers shown

Title
Deep neural operators can serve as accurate surrogates for shape optimization: A case study for airfoils K. Shukla Vivek Oommen Ahmad Peyvan Michael Penwarden L. Bravo A. Ghoshal Robert M. Kirby George Karniadakis 33 19 0 02 Feb 2023
Partial Differential Equations Meet Deep Neural Networks: A Survey Shudong Huang Wentao Feng Chenwei Tang Jiancheng Lv AI4CE AIMat 24 17 0 27 Oct 2022
Multi-fidelity wavelet neural operator with application to uncertainty quantification A. Thakur Tapas Tripura S. Chakraborty 24 12 0 11 Aug 2022
Multi-fidelity surrogate modeling using long short-term memory networks Paolo Conti Mengwu Guo Andrea Manzoni J. Hesthaven AI4CE 28 48 0 05 Aug 2022
Unsupervised Legendre-Galerkin Neural Network for Singularly Perturbed Partial Differential Equations Junho Choi N. Kim Youngjoon Hong AI4CE 16 0 0 21 Jul 2022
Physics-informed neural networks with hard constraints for inverse design Lu Lu R. Pestourie Wenjie Yao Zhicheng Wang F. Verdugo Steven G. Johnson PINN 39 493 0 09 Feb 2021
Fourier 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 203 2,281 0 18 Oct 2020