Scalable Uncertainty Quantification for Deep Operator Networks using Randomized Priors

6 March 2022

Papers citing "Scalable Uncertainty Quantification for Deep Operator Networks using Randomized Priors"

34 / 34 papers shown

Title
Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning Amirhossein Mollaali Christian Moya Amanda A. Howard Alexander Heinlein P. Stinis Guang Lin 26 0 0 21 Apr 2025
Active Learning Design: Modeling Force Output for Axisymmetric Soft Pneumatic Actuators Gregory M. Campbell Gentian Muhaxheri Leonardo Ferreira Guilhoto Christian D. Santangelo P. Perdikaris James H. Pikul Mark H. Yim 35 0 0 01 Apr 2025
Data-Driven Probabilistic Air-Sea Flux Parameterization Jiarong Wu Pavel Perezhogin D. Gagne Brandon Reichl Aneesh C. Subramanian Elizabeth Thompson Laure Zanna 39 0 0 06 Mar 2025
Cauchy Random Features for Operator Learning in Sobolev Space Chunyang Liao Deanna Needell Hayden Schaeffer 34 0 0 01 Mar 2025
Federated scientific machine learning for approximating functions and solving differential equations with data heterogeneity Handi Zhang Langchen Liu Lu Lu FedML 32 1 0 17 Oct 2024
Time-Series Forecasting, Knowledge Distillation, and Refinement within a Multimodal PDE Foundation Model Derek Jollie Jingmin Sun Zecheng Zhang Hayden Schaeffer AI4TS 47 3 0 17 Sep 2024
Empowering Bayesian Neural Networks with Functional Priors through Anchored Ensembling for Mechanics Surrogate Modeling Applications Javad Ghorbanian Nicholas Casaprima Audrey Olivier 28 0 0 08 Sep 2024
Learning Latent Space Dynamics with Model-Form Uncertainties: A Stochastic Reduced-Order Modeling Approach Jin Yi Yong Rudy Geelen Johann Guilleminot 28 1 0 30 Aug 2024
Alpha-VI DeepONet: A prior-robust variational Bayesian approach for enhancing DeepONets with uncertainty quantification Soban Nasir Lone Subhayan De R. Nayek BDL 34 1 0 01 Aug 2024
Composite Bayesian Optimization In Function Spaces Using NEON -- Neural Epistemic Operator Networks Leonardo Ferreira Guilhoto P. Perdikaris BDL 38 1 0 03 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 40 2 0 15 Mar 2024
Uncertainty quantification for deeponets with ensemble kalman inversion Andrew Pensoneault Xueyu Zhu 23 1 0 06 Mar 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 49 17 0 23 Feb 2024
RiemannONets: Interpretable Neural Operators for Riemann Problems Ahmad Peyvan Vivek Oommen Ameya Dilip Jagtap George Karniadakis AI4CE 36 22 0 16 Jan 2024
Uncertainty quantification for noisy inputs-outputs in physics-informed neural networks and neural operators Zongren Zou Xuhui Meng George Karniadakis AI4CE 33 19 0 19 Nov 2023
Ensemble models outperform single model uncertainties and predictions for operator-learning of hypersonic flows Victor J. Leon Noah Ford Honest Mrema Jeffrey Gilbert Alexander New UQCV AI4CE 15 0 0 31 Oct 2023
D2NO: Efficient Handling of Heterogeneous Input Function Spaces with Distributed Deep Neural Operators Zecheng Zhang Christian Moya Lu Lu Guang Lin Hayden Schaeffer 29 11 0 29 Oct 2023
Multi-fidelity climate model parameterization for better generalization and extrapolation Mohamed Aziz Bhouri Liran Peng Michael S. Pritchard Pierre Gentine AI4CE 26 4 0 19 Sep 2023
A hybrid Decoder-DeepONet operator regression framework for unaligned observation data Bo Chen Chenyu Wang Weipeng Li Haiyang Fu 28 9 0 18 Aug 2023
Scalable Bayesian optimization with high-dimensional outputs using randomized prior networks Mohamed Aziz Bhouri M. Joly Robert Yu S. Sarkar P. Perdikaris BDL UQCV AI4CE 19 1 0 14 Feb 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 13 11 0 07 Feb 2023
Randomized prior wavelet neural operator for uncertainty quantification Shailesh Garg S. Chakraborty UQCV BDL 15 1 0 02 Feb 2023
On Approximating the Dynamic Response of Synchronous Generators via Operator Learning: A Step Towards Building Deep Operator-based Power Grid Simulators Christian Moya Guang Lin Tianqiao Zhao Meng Yue 32 8 0 29 Jan 2023
Reliable extrapolation of deep neural operators informed by physics or sparse observations Min Zhu Handi Zhang Anran Jiao George Karniadakis Lu Lu 42 91 0 13 Dec 2022
Variationally Mimetic Operator Networks Dhruv V. Patel Deep Ray M. Abdelmalik T. Hughes Assad A. Oberai 55 23 0 26 Sep 2022
NeuralUQ: A comprehensive library for uncertainty quantification in neural differential equations and operators Zongren Zou Xuhui Meng Apostolos F. Psaros George Karniadakis AI4CE 30 36 0 25 Aug 2022
Variational Bayes Deep Operator Network: A data-driven Bayesian solver for parametric differential equations Shailesh Garg S. Chakraborty 27 6 0 12 Jun 2022
Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of nanoscale heat transport Lu Lu R. Pestourie Steven G. Johnson Giuseppe Romano AI4CE 16 102 0 14 Apr 2022
Improved architectures and training algorithms for deep operator networks Sizhuang He Hanwen Wang P. Perdikaris AI4CE 49 105 0 04 Oct 2021
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 131 438 0 18 Dec 2020
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 217 2,287 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 183 759 0 13 Mar 2020
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 276 5,661 0 05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,138 0 06 Jun 2015