Adversarial Uncertainty Quantification in Physics-Informed Neural Networks

9 November 2018

Papers citing "Adversarial Uncertainty Quantification in Physics-Informed Neural Networks"

35 / 135 papers shown

Title
A Physics-Informed Deep Learning Paradigm for Car-Following Models Zhaobin Mo Xuan Di Rongye Shi PINN AI4CE 30 132 0 24 Dec 2020
On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks Sizhuang He Hanwen Wang P. Perdikaris 133 441 0 18 Dec 2020
SRoll3: A neural network approach to reduce large-scale systematic effects in the Planck High Frequency Instrument maps Manuel López-Radcenco J. Delouis L. Vibert 6 2 0 17 Dec 2020
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges Moloud Abdar Farhad Pourpanah Sadiq Hussain Dana Rezazadegan Li Liu ... Xiaochun Cao Abbas Khosravi U. Acharya V. Makarenkov S. Nahavandi BDL UQCV 56 1,883 0 12 Nov 2020
Exploring the potential of transfer learning for metamodels of heterogeneous material deformation Emma Lejeune Bill Zhao AI4CE 6 20 0 28 Oct 2020
Living in the Physics and Machine Learning Interplay for Earth Observation Gustau Camps-Valls D. Svendsen Jordi Cortés-Andrés Álvaro Moreno-Martínez Adrián Pérez-Suay J. Adsuara I. Martín M. Piles Jordi Munoz-Marí Luca Martino PINN AI4CE 12 6 0 18 Oct 2020
Recurrent convolutional neural network for the surrogate modeling of subsurface flow simulation Hyung Jun Yang Timothy Yeo J. An AI4CE 14 1 0 08 Oct 2020
Mutual Information for Explainable Deep Learning of Multiscale Systems S. Taverniers E. Hall Markos A. Katsoulakis D. Tartakovsky 16 15 0 07 Sep 2020
Adaptive Physics-Informed Neural Networks for Markov-Chain Monte Carlo M. A. Nabian Hadi Meidani 14 6 0 03 Aug 2020
When and why PINNs fail to train: A neural tangent kernel perspective Sizhuang He Xinling Yu P. Perdikaris 33 881 0 28 Jul 2020
A Survey on Autonomous Vehicle Control in the Era of Mixed-Autonomy: From Physics-Based to AI-Guided Driving Policy Learning Xuan Di Rongye Shi 29 174 0 10 Jul 2020
GINNs: Graph-Informed Neural Networks for Multiscale Physics E. Hall S. Taverniers Markos A. Katsoulakis D. Tartakovsky PINN AI4CE 12 30 0 26 Jun 2020
Physics informed deep learning for computational elastodynamics without labeled data Chengping Rao Hao Sun Yang Liu PINN AI4CE 24 222 0 10 Jun 2020
Multi-fidelity Generative Deep Learning Turbulent Flows N. Geneva N. Zabaras AI4CE 21 44 0 08 Jun 2020
Deep learning of free boundary and Stefan problems Sizhuang He P. Perdikaris 27 80 0 04 Jun 2020
A Linear Algebraic Approach to Model Parallelism in Deep Learning Russell J. Hewett Thomas J. Grady FedML 20 16 0 04 Jun 2020
Inverse Estimation of Elastic Modulus Using Physics-Informed Generative Adversarial Networks James E. Warner Julian Cuevas Geoffrey F. Bomarito Patrick E. Leser W. Leser GAN 23 10 0 20 May 2020
Physics-informed learning of governing equations from scarce data Zhao Chen Yang Liu Hao Sun PINN AI4CE 11 380 0 05 May 2020
Bayesian differential programming for robust systems identification under uncertainty Yibo Yang Mohamed Aziz Bhouri P. Perdikaris OOD 33 32 0 15 Apr 2020
A non-cooperative meta-modeling game for automated third-party calibrating, validating, and falsifying constitutive laws with parallelized adversarial attacks Kun Wang WaiChing Sun Q. Du 22 22 0 13 Apr 2020
Learning To Solve Differential Equations Across Initial Conditions Shehryar Malik Usman Anwar Ali Ahmed Alireza Aghasi AI4CE 9 8 0 26 Mar 2020
Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems J. Willard X. Jia Shaoming Xu M. Steinbach Vipin Kumar AI4CE 91 389 0 10 Mar 2020
Variational inference formulation for a model-free simulation of a dynamical system with unknown parameters by a recurrent neural network K. Yeo D. E. C. Grullon Fan-Keng Sun Duane S. Boning Jayant Kalagnanam BDL 26 3 0 02 Mar 2020
Connecting GANs, MFGs, and OT Haoyang Cao Xin Guo Mathieu Laurière GAN 26 14 0 10 Feb 2020
Physics Informed Deep Learning for Transport in Porous Media. Buckley Leverett Problem Cedric G. Fraces Adrien Papaioannou H. Tchelepi AI4CE PINN 25 19 0 15 Jan 2020
Understanding and mitigating gradient pathologies in physics-informed neural networks Sizhuang He Yujun Teng P. Perdikaris AI4CE PINN 42 290 0 13 Jan 2020
Sparse Polynomial Chaos expansions using Variational Relevance Vector Machines Panagiotis Tsilifis I. Papaioannou D. Štraub F. Nobile 21 18 0 23 Dec 2019
Density Propagation with Characteristics-based Deep Learning Tenavi Nakamura-Zimmerer D. Venturi Q. Gong W. Kang AI4CE 14 1 0 21 Nov 2019
Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs Liu Yang Sean Treichler Thorsten Kurth Keno Fischer D. Barajas-Solano ... Valentin Churavy A. Tartakovsky Michael Houston P. Prabhat George Karniadakis AI4CE 47 38 0 29 Oct 2019
PPINN: Parareal Physics-Informed Neural Network for time-dependent PDEs Xuhui Meng Zhen Li Dongkun Zhang George Karniadakis PINN AI4CE 22 443 0 23 Sep 2019
Physics-Informed Machine Learning Models for Predicting the Progress of Reactive-Mixing M. Mudunuru S. Karra 25 11 0 28 Aug 2019
Modeling the Dynamics of PDE Systems with Physics-Constrained Deep Auto-Regressive Networks N. Geneva N. Zabaras AI4CE 10 268 0 13 Jun 2019
Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks Georgios Kissas Yibo Yang E. Hwuang W. Witschey John A. Detre P. Perdikaris AI4CE 13 365 0 13 May 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 39 626 0 29 Mar 2019
Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data Yinhao Zhu N. Zabaras P. Koutsourelakis P. Perdikaris PINN AI4CE 46 854 0 18 Jan 2019