NeuralUQ: A comprehensive library for uncertainty quantification in neural differential equations and operators

25 August 2022

Papers citing "NeuralUQ: A comprehensive library for uncertainty quantification in neural differential equations and operators"

30 / 30 papers shown

Title
Scalable physics-informed deep generative model for solving forward and inverse stochastic differential equations Shaoqian Zhou Wen You Ling Guo Xuhui Meng DiffM MedIm 51 0 0 23 Mar 2025
Learning and discovering multiple solutions using physics-informed neural networks with random initialization and deep ensemble Zongren Zou Zhicheng Wang George Karniadakis PINN AI4CE 65 2 0 08 Mar 2025
Physics-informed deep learning for infectious disease forecasting Y. Qian Éric Marty Avranil Basu Avranil Basu Eamon B. O'Dea Xianqiao Wang Spencer Fox Pejman Rohani John M. Drake He Li PINN AI4CE 76 2 0 16 Jan 2025
Towards certifiable AI in aviation: landscape, challenges, and opportunities Hymalai Bello Daniel Geißler L. Ray Stefan Muller-Divéky Peter Muller Shannon Kittrell Mengxi Liu Bo Zhou Paul Lukowicz 27 1 0 13 Sep 2024
Quantification of total uncertainty in the physics-informed reconstruction of CVSim-6 physiology Mario De Florio Zongren Zou Daniele E. Schiavazzi George Karniadakis 24 3 0 13 Aug 2024
Linearization Turns Neural Operators into Function-Valued Gaussian Processes Emilia Magnani Marvin Pfortner Tobias Weber Philipp Hennig UQCV 55 1 0 07 Jun 2024
Large scale scattering using fast solvers based on neural operators Zongren Zou Adar Kahana Enrui Zhang Eli Turkel Rishikesh Ranade Jay Pathak George Karniadakis 23 1 0 20 May 2024
Leveraging viscous Hamilton-Jacobi PDEs for uncertainty quantification in scientific machine learning Zongren Zou Tingwei Meng Paula Chen Jérome Darbon George Karniadakis 22 7 0 12 Apr 2024
Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks Christian Moya Amirhossein Mollaali Zecheng Zhang Lu Lu Guang Lin UQCV 47 17 0 23 Feb 2024
Uncertainty quantification for noisy inputs-outputs in physics-informed neural networks and neural operators Zongren Zou Xuhui Meng George Karniadakis AI4CE 17 19 0 19 Nov 2023
Evaluating Uncertainty Quantification approaches for Neural PDEs in scientific applications Vardhan Dongre G. S. Hora UQCV AI4CE 11 0 0 08 Nov 2023
PICProp: Physics-Informed Confidence Propagation for Uncertainty Quantification Qianli Shen Wai Hoh Tang Zhun Deng Apostolos F. Psaros Kenji Kawaguchi 23 1 0 10 Oct 2023
Artificial to Spiking Neural Networks Conversion for Scientific Machine Learning Qian Zhang Chen-Chun Wu Adar Kahana Youngeun Kim Yuhang Li George Karniadakis Priyadarshini Panda 14 9 0 31 Aug 2023
Discovering a reaction-diffusion model for Alzheimer's disease by combining PINNs with symbolic regression Zhen Zhang Zongren Zou E. Kuhl George Karniadakis 13 41 0 16 Jul 2023
Evaluation of machine learning architectures on the quantification of epistemic and aleatoric uncertainties in complex dynamical systems Stephen Guth A. Mojahed T. Sapsis AI4CE 10 2 0 27 Jun 2023
UQpy v4.1: Uncertainty Quantification with Python Dimitrios Tsapetis Michael D. Shields Dimitris G. Giovanis Audrey Olivier Lukás Novák ... Mohit Chauhan Katiana Kontolati Lohit Vandanapu Dimitrios Loukrezis Michael Gardner GP 14 11 0 16 May 2023
A Generative Modeling Framework for Inferring Families of Biomechanical Constitutive Laws in Data-Sparse Regimes Minglang Yin Zongren Zou Enrui Zhang C. Cavinato J. Humphrey George Karniadakis SyDa MedIm AI4CE 37 11 0 04 May 2023
Leveraging Multi-time Hamilton-Jacobi PDEs for Certain Scientific Machine Learning Problems Paula Chen Tingwei Meng Zongren Zou Jérome Darbon George Karniadakis AI4CE 20 5 0 22 Mar 2023
Efficient Bayesian Physics Informed Neural Networks for Inverse Problems via Ensemble Kalman Inversion Andrew Pensoneault Xueyu Zhu PINN 11 5 0 13 Mar 2023
IB-UQ: Information bottleneck based uncertainty quantification for neural function regression and neural operator learning Ling Guo Hao Wu Wenwen Zhou Yan Wang Tao Zhou UQCV 11 11 0 07 Feb 2023
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 20 19 0 02 Feb 2023
L-HYDRA: Multi-Head Physics-Informed Neural Networks Zongren Zou George Karniadakis AI4CE 11 26 0 05 Jan 2023
Error-Aware B-PINNs: Improving Uncertainty Quantification in Bayesian Physics-Informed Neural Networks Olga Graf P. Flores P. Protopapas K. Pichara PINN 19 6 0 14 Dec 2022
Physics-informed neural networks for operator equations with stochastic data Paul Escapil-Inchauspé G. A. Ruz 8 2 0 15 Nov 2022
Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantification Youngseog Chung I. Char Han Guo J. Schneider W. Neiswanger 30 70 0 21 Sep 2021
$NVIDIA SimNet^{TM}: an AI-accelerated multi-physics simulation framework$ NVIDIA SimNet^{TM}: an AI-accelerated multi-physics simulation framework O. Hennigh S. Narasimhan M. A. Nabian Akshay Subramaniam Kaustubh Tangsali M. Rietmann J. Ferrandis Wonmin Byeon Z. Fang S. Choudhry PINN AI4CE 91 125 0 14 Dec 2020
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data Liu Yang Xuhui Meng George Karniadakis PINN 170 755 0 13 Mar 2020
Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems J. Willard X. Jia Shaoming Xu M. Steinbach Vipin Kumar AI4CE 80 385 0 10 Mar 2020
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 268 5,635 0 05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 247 9,042 0 06 Jun 2015