Physics-Driven Regularization of Deep Neural Networks for Enhanced
Engineering Design and Analysis

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Physics-Driven Regularization of Deep Neural Networks for Enhanced Engineering Design and Analysis

11 October 2018

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Papers citing "Physics-Driven Regularization of Deep Neural Networks for Enhanced Engineering Design and Analysis"

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Title
Demystifying the Data Need of ML-surrogates for CFD Simulations Tongtao Zhang Biswadip Dey Krishna Veeraraghavan Harshad Kulkarni Amit Chakraborty AI4CE 40 5 0 05 May 2022
A Deep Collocation Method for the Bending Analysis of Kirchhoff Plate Hongwei Guo X. Zhuang Timon Rabczuk AI4CE 60 440 0 04 Feb 2021
Time-Reversal Symmetric ODE Network In Huh Eunho Yang Sung Ju Hwang Jinwoo Shin 100 20 0 22 Jul 2020
Physics-Informed Neural Networks for Multiphysics Data Assimilation with Application to Subsurface Transport Qizhi He D. Barajas-Solano G. Tartakovsky A. Tartakovsky AI4CE 64 267 0 06 Dec 2019
A deep surrogate approach to efficient Bayesian inversion in PDE and integral equation models Teo Deveney Amelia Gosse Peter Du 87 9 0 03 Oct 2019