Physics-informed deep generative models

Physics-informed deep generative models

9 December 2018

Papers citing "Physics-informed deep generative models"

14 / 14 papers shown

Title
GD-VAEs: Geometric Dynamic Variational Autoencoders for Learning Nonlinear Dynamics and Dimension Reductions Ryan Lopez P. Atzberger AI4CE 24 7 0 10 Jun 2022
A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic Barbara Z Cunha C. Droz A. Zine Stéphane Foulard M. Ichchou AI4CE 27 84 0 13 Apr 2022
Scalable Uncertainty Quantification for Deep Operator Networks using Randomized Priors Yibo Yang Georgios Kissas P. Perdikaris BDL UQCV 20 40 0 06 Mar 2022
Improved architectures and training algorithms for deep operator networks Sifan Wang Hanwen Wang P. Perdikaris AI4CE 42 105 0 04 Oct 2021
Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling Naoya Takeishi Alexandros Kalousis DRL AI4CE 22 54 0 25 Feb 2021
POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition S. Fresca Andrea Manzoni AI4CE 11 212 0 28 Jan 2021
Data-driven Identification of 2D Partial Differential Equations using extracted physical features Kazem Meidani A. Farimani 13 17 0 20 Oct 2020
Energy-based error bound of physics-informed neural network solutions in elasticity Mengwu Guo E. Haghighat PINN 36 28 0 18 Oct 2020
When and why PINNs fail to train: A neural tangent kernel perspective Sifan Wang Xinling Yu P. Perdikaris 28 874 0 28 Jul 2020
Expressivity of Deep Neural Networks Ingo Gühring Mones Raslan Gitta Kutyniok 16 50 0 09 Jul 2020
Deep learning of free boundary and Stefan problems Sifan Wang P. Perdikaris 19 80 0 04 Jun 2020
A Theoretical Analysis of Deep Neural Networks and Parametric PDEs Gitta Kutyniok P. Petersen Mones Raslan R. Schneider 13 197 0 31 Mar 2019
Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems Laura von Rueden S. Mayer Katharina Beckh B. Georgiev Sven Giesselbach ... Rajkumar Ramamurthy Michal Walczak Jochen Garcke Christian Bauckhage Jannis Schuecker 34 626 0 29 Mar 2019
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 261 9,136 0 06 Jun 2015