Uncertainty quantification for noisy inputs-outputs in physics-informed neural networks and neural operators

19 November 2023

Papers citing "Uncertainty quantification for noisy inputs-outputs in physics-informed neural networks and neural operators"

20 / 20 papers shown

Title
Improved Uncertainty Quantification in Physics-Informed Neural Networks Using Error Bounds and Solution Bundles P. Flores Olga Graf P. Protopapas K. Pichara PINN 16 0 0 09 May 2025
Position: Epistemic Artificial Intelligence is Essential for Machine Learning Models to Know When They Do Not Know Shireen Kudukkil Manchingal Fabio Cuzzolin 42 0 0 08 May 2025
Learning Dual-Arm Coordination for Grasping Large Flat Objects Yongliang Wang H. Kasaei 32 0 0 04 Apr 2025
Uncertainty propagation in feed-forward neural network models Jeremy Diamzon Daniele Venturi 57 0 0 27 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 78 2 0 16 Jan 2025
HJ-sampler: A Bayesian sampler for inverse problems of a stochastic process by leveraging Hamilton-Jacobi PDEs and score-based generative models Tingwei Meng Zongren Zou Jérome Darbon George Karniadakis DiffM 35 2 0 15 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 26 3 0 13 Aug 2024
NeuroSEM: A hybrid framework for simulating multiphysics problems by coupling PINNs and spectral elements K. Shukla Zongren Zou Chi Hin Chan Additi Pandey Zhicheng Wang George Karniadakis PINN 37 7 0 30 Jul 2024
Large language models, physics-based modeling, experimental measurements: the trinity of data-scarce learning of polymer properties Ning Liu S. Jafarzadeh B. Lattimer Shuna Ni Jim Lua Yue Yu AI4CE 35 1 0 03 Jul 2024
A comprehensive and FAIR comparison between MLP and KAN representations for differential equations and operator networks K. Shukla Juan Diego Toscano Zhicheng Wang Zongren Zou George Karniadakis 24 73 0 05 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 28 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 24 7 0 12 Apr 2024
Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs S. C. Mouli Danielle C. Maddix S. Alizadeh Gaurav Gupta Andrew Stuart Michael W. Mahoney Yuyang Wang UQCV AI4CE 35 2 0 15 Mar 2024
Uncertainty quantification for deeponets with ensemble kalman inversion Andrew Pensoneault Xueyu Zhu 19 1 0 06 Mar 2024
Calibrated Uncertainty Quantification for Operator Learning via Conformal Prediction Ziqi Ma Kamyar Azizzadenesheli A. Anandkumar 8 6 0 02 Feb 2024
Leveraging Multi-time Hamilton-Jacobi PDEs for Certain Scientific Machine Learning Problems Paula Chen Tingwei Meng Zongren Zou Jérome Darbon George Karniadakis AI4CE 25 5 0 22 Mar 2023
Physics-informed Neural Networks with Unknown Measurement Noise Philipp Pilar Niklas Wahlström PINN 18 6 0 28 Nov 2022
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 2,272 0 18 Oct 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