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Scalable Uncertainty Quantification for Deep Operator Networks using
  Randomized Priors

Scalable Uncertainty Quantification for Deep Operator Networks using Randomized Priors

Computer Methods in Applied Mechanics and Engineering (CMAME), 2022
6 March 2022
Jianlong Wu
Georgios Kissas
P. Perdikaris
    BDLUQCV
ArXiv (abs)PDFHTML

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

16 / 16 papers shown
LVM-GP: Uncertainty-Aware PDE Solver via coupling latent variable model and Gaussian process
LVM-GP: Uncertainty-Aware PDE Solver via coupling latent variable model and Gaussian process
Xiaodong Feng
Ling Guo
Xiaoliang Wan
Hao Wu
Tao Zhou
Wenwen Zhou
AI4CE
227
1
0
30 Jul 2025
Towards Robust Surrogate Models: Benchmarking Machine Learning Approaches to Expediting Phase Field Simulations of Brittle Fracture
Towards Robust Surrogate Models: Benchmarking Machine Learning Approaches to Expediting Phase Field Simulations of Brittle Fracture
Erfan Hamdi
Emma Lejeune
OODAI4CE
247
1
0
09 Jul 2025
Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning
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
236
3
0
21 Apr 2025
Data-Driven Probabilistic Air-Sea Flux Parameterization
Data-Driven Probabilistic Air-Sea Flux Parameterization
Jiarong Wu
Pavel Perezhogin
D. Gagne
Brandon Reichl
Aneesh C. Subramanian
Elizabeth Thompson
Laure Zanna
355
0
0
06 Mar 2025
Cauchy Random Features for Operator Learning in Sobolev Space
Cauchy Random Features for Operator Learning in Sobolev Space
Chunyang Liao
Deanna Needell
Hayden Schaeffer
431
3
0
01 Mar 2025
Uncertainty quantification for deeponets with ensemble kalman inversion
Uncertainty quantification for deeponets with ensemble kalman inversion
Andrew Pensoneault
Xueyu Zhu
251
6
0
06 Mar 2024
Uncertainty quantification for noisy inputs-outputs in physics-informed
  neural networks and neural operators
Uncertainty quantification for noisy inputs-outputs in physics-informed neural networks and neural operators
Zongren Zou
Xuhui Meng
George Karniadakis
AI4CE
272
35
0
19 Nov 2023
Multi-fidelity climate model parameterization for better generalization
  and extrapolation
Multi-fidelity climate model parameterization for better generalization and extrapolation
Mohamed Aziz Bhouri
Liran Peng
Michael S. Pritchard
Pierre Gentine
AI4CE
245
7
0
19 Sep 2023
IB-UQ: Information bottleneck based uncertainty quantification for
  neural function regression and neural operator learning
IB-UQ: Information bottleneck based uncertainty quantification for neural function regression and neural operator learningJournal of Computational Physics (JCP), 2023
Ling Guo
Hao Wu
Wenwen Zhou
Yan Wang
Tao Zhou
UQCV
313
21
0
07 Feb 2023
Randomized prior wavelet neural operator for uncertainty quantification
Randomized prior wavelet neural operator for uncertainty quantificationProbabilistic Engineering Mechanics (PEM), 2023
Shailesh Garg
S. Chakraborty
UQCVBDL
199
2
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
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
Amirthagunaraj Yogarathnam
Meng Yue
174
13
0
29 Jan 2023
Reliable extrapolation of deep neural operators informed by physics or
  sparse observations
Reliable extrapolation of deep neural operators informed by physics or sparse observationsSocial Science Research Network (SSRN), 2022
Min Zhu
Handi Zhang
Anran Jiao
George Karniadakis
Lu Lu
258
127
0
13 Dec 2022
Variationally Mimetic Operator Networks
Variationally Mimetic Operator NetworksComputer Methods in Applied Mechanics and Engineering (CMAME), 2022
Dhruv V. Patel
Deep Ray
M. Abdelmalik
T. Hughes
Assad A. Oberai
319
33
0
26 Sep 2022
NeuralUQ: A comprehensive library for uncertainty quantification in
  neural differential equations and operators
NeuralUQ: A comprehensive library for uncertainty quantification in neural differential equations and operators
Zongren Zou
Xuhui Meng
Apostolos F. Psaros
George Karniadakis
AI4CE
221
46
0
25 Aug 2022
Variational Bayes Deep Operator Network: A data-driven Bayesian solver
  for parametric differential equations
Variational Bayes Deep Operator Network: A data-driven Bayesian solver for parametric differential equations
Shailesh Garg
S. Chakraborty
237
9
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
Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of nanoscale heat transportPhysical Review Research (Phys. Rev. Res.), 2022
Lu Lu
R. Pestourie
Steven G. Johnson
Giuseppe Romano
AI4CE
236
148
0
14 Apr 2022
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