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Scalable and efficient algorithms for the propagation of uncertainty
  from data through inference to prediction for large-scale problems, with
  application to flow of the Antarctic ice sheet
v1v2 (latest)

Scalable and efficient algorithms for the propagation of uncertainty from data through inference to prediction for large-scale problems, with application to flow of the Antarctic ice sheet

Journal of Computational Physics (JCP), 2014
5 October 2014
T. Isaac
N. Petra
G. Stadler
Omar Ghattas
    PINN
ArXiv (abs)PDFHTML

Papers citing "Scalable and efficient algorithms for the propagation of uncertainty from data through inference to prediction for large-scale problems, with application to flow of the Antarctic ice sheet"

28 / 28 papers shown
Domain-Decomposed Graph Neural Network Surrogate Modeling for Ice Sheets
Domain-Decomposed Graph Neural Network Surrogate Modeling for Ice Sheets
Adrienne M. Propp
M. Perego
E. Cyr
Anthony Gruber
Amanda A. Howard
Alexander Heinlein
P. Stinis
D. Tartakovsky
AI4CE
194
0
0
01 Dec 2025
Adaptive Sensor Steering Strategy Using Deep Reinforcement Learning for Dynamic Data Acquisition in Digital Twins
Adaptive Sensor Steering Strategy Using Deep Reinforcement Learning for Dynamic Data Acquisition in Digital Twins
Collins O. Ogbodo
Timothy J. Rogers
Mattia Dal Borgo
David J. Wagg
366
1
0
14 Apr 2025
Stratospheric aerosol source inversion: Noise, variability, and
  uncertainty quantification
Stratospheric aerosol source inversion: Noise, variability, and uncertainty quantificationJournal of Machine Learning for Modeling and Computing (JMLMC), 2024
J. Hart
I. Manickam
M. Gulian
L. Swiler
D. Bull
T. Ehrmann
H. Brown
B. Wagman
J. Watkins
232
6
0
10 Sep 2024
Sampling via Gradient Flows in the Space of Probability Measures
Sampling via Gradient Flows in the Space of Probability Measures
Yifan Chen
Daniel Zhengyu Huang
Jiaoyang Huang
Sebastian Reich
Andrew M. Stuart
477
20
0
05 Oct 2023
Solving High-Dimensional Inverse Problems with Auxiliary Uncertainty via
  Operator Learning with Limited Data
Solving High-Dimensional Inverse Problems with Auxiliary Uncertainty via Operator Learning with Limited DataJournal of Machine Learning for Modeling and Computing (JMLMC), 2023
Joseph L. Hart
Mamikon A. Gulian
Indu Manickam
L. Swiler
233
11
0
20 Mar 2023
Gradient Flows for Sampling: Mean-Field Models, Gaussian Approximations
  and Affine Invariance
Gradient Flows for Sampling: Mean-Field Models, Gaussian Approximations and Affine Invariance
Yifan Chen
Daniel Zhengyu Huang
Jiaoyang Huang
Sebastian Reich
Andrew M. Stuart
849
22
0
21 Feb 2023
A Hybrid Deep Neural Operator/Finite Element Method for Ice-Sheet
  Modeling
A Hybrid Deep Neural Operator/Finite Element Method for Ice-Sheet ModelingJournal of Computational Physics (JCP), 2023
Qizhi He
M. Perego
Amanda A. Howard
George Karniadakis
P. Stinis
220
20
0
26 Jan 2023
Differentiable modeling to unify machine learning and physical models
  and advance Geosciences
Differentiable modeling to unify machine learning and physical models and advance Geosciences
Chaopeng Shen
A. Appling
Pierre Gentine
Toshiyuki Bandai
H. Gupta
...
Chris Rackauckas
Tirthankar Roy
Chonggang Xu
Binayak Mohanty
K. Lawson
AI4CE
380
18
0
10 Jan 2023
Optimal design of large-scale nonlinear Bayesian inverse problems under
  model uncertainty
Optimal design of large-scale nonlinear Bayesian inverse problems under model uncertaintyInverse Problems (IP), 2022
A. Alexanderian
R. Nicholson
N. Petra
286
17
0
08 Nov 2022
Residual-based error correction for neural operator accelerated
  infinite-dimensional Bayesian inverse problems
Residual-based error correction for neural operator accelerated infinite-dimensional Bayesian inverse problemsJournal of Computational Physics (JCP), 2022
Lianghao Cao
Thomas O'Leary-Roseberry
Prashant K. Jha
J. Oden
Omar Ghattas
335
35
0
06 Oct 2022
Derivative-Informed Neural Operator: An Efficient Framework for
  High-Dimensional Parametric Derivative Learning
Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative LearningJournal of Computational Physics (JCP), 2022
Thomas O'Leary-Roseberry
Peng Chen
Umberto Villa
Omar Ghattas
AI4CE
374
59
0
21 Jun 2022
hIPPYlib-MUQ: A Bayesian Inference Software Framework for Integration of
  Data with Complex Predictive Models under Uncertainty
hIPPYlib-MUQ: A Bayesian Inference Software Framework for Integration of Data with Complex Predictive Models under Uncertainty
Ki-tae Kim
Umberto Villa
M. Parno
Youssef Marzouk
Omar Ghattas
N. Petra
257
25
0
01 Dec 2021
Variational Inference at Glacier Scale
Variational Inference at Glacier Scale
D. Brinkerhoff
BDL
183
16
0
16 Aug 2021
Derivative-Informed Projected Neural Networks for High-Dimensional
  Parametric Maps Governed by PDEs
Derivative-Informed Projected Neural Networks for High-Dimensional Parametric Maps Governed by PDEsComputer Methods in Applied Mechanics and Engineering (CMAME), 2020
Thomas O'Leary-Roseberry
Umberto Villa
Peng Chen
Omar Ghattas
360
78
0
30 Nov 2020
Taylor approximation for chance constrained optimization problems
  governed by partial differential equations with high-dimensional random
  parameters
Taylor approximation for chance constrained optimization problems governed by partial differential equations with high-dimensional random parameters
Peng Chen
Omar Ghattas
250
22
0
19 Nov 2020
Multilevel Hierarchical Decomposition of Finite Element White Noise with
  Application to Multilevel Markov Chain Monte Carlo
Multilevel Hierarchical Decomposition of Finite Element White Noise with Application to Multilevel Markov Chain Monte CarloSIAM Journal on Scientific Computing (SIAM J. Sci. Comput.), 2020
Hillary R. Fairbanks
U. Villa
P. Vassilevski
221
8
0
28 Jul 2020
The SPDE Approach to Matérn Fields: Graph Representations
The SPDE Approach to Matérn Fields: Graph RepresentationsStatistical Science (Statist. Sci.), 2020
D. Sanz-Alonso
Ruiyi Yang
501
23
0
16 Apr 2020
Stability of Gibbs Posteriors from the Wasserstein Loss for Bayesian
  Full Waveform Inversion
Stability of Gibbs Posteriors from the Wasserstein Loss for Bayesian Full Waveform Inversion
Matthew M. Dunlop
Yunan Yang
409
13
0
07 Apr 2020
Projected Stein Variational Gradient Descent
Projected Stein Variational Gradient DescentNeural Information Processing Systems (NeurIPS), 2020
Peng Chen
Omar Ghattas
BDL
540
78
0
09 Feb 2020
BIMC: The Bayesian Inverse Monte Carlo method for goal-oriented
  uncertainty quantification. Part I
BIMC: The Bayesian Inverse Monte Carlo method for goal-oriented uncertainty quantification. Part I
Siddhant Wahal
George Biros
205
8
0
02 Nov 2019
Multiplicative noise in Bayesian inverse problems: Well-posedness and
  consistency of MAP estimators
Multiplicative noise in Bayesian inverse problems: Well-posedness and consistency of MAP estimators
Matthew M. Dunlop
204
7
0
31 Oct 2019
Sampling of Bayesian posteriors with a non-Gaussian probabilistic
  learning on manifolds from a small dataset
Sampling of Bayesian posteriors with a non-Gaussian probabilistic learning on manifolds from a small datasetStatistics and computing (Stat. Comput.), 2019
Christian Soize
R. Ghanem
274
21
0
28 Oct 2019
hIPPYlib: An Extensible Software Framework for Large-Scale Inverse
  Problems Governed by PDEs; Part I: Deterministic Inversion and Linearized
  Bayesian Inference
hIPPYlib: An Extensible Software Framework for Large-Scale Inverse Problems Governed by PDEs; Part I: Deterministic Inversion and Linearized Bayesian InferenceACM Transactions on Mathematical Software (TOMS), 2019
Umberto Villa
N. Petra
Omar Ghattas
169
73
0
09 Sep 2019
Bayesian Inference with Generative Adversarial Network Priors
Bayesian Inference with Generative Adversarial Network Priors
Dhruv V. Patel
Assad A. Oberai
GANAI4CE
176
20
0
22 Jul 2019
Efficient Marginalization-based MCMC Methods for Hierarchical Bayesian
  Inverse Problems
Efficient Marginalization-based MCMC Methods for Hierarchical Bayesian Inverse Problems
A. Saibaba
Johnathan M. Bardsley
D. Brown
A. Alexanderian
268
14
0
02 Nov 2018
Analysis of boundary effects on PDE-based sampling of Whittle-Matérn
  random fields
Analysis of boundary effects on PDE-based sampling of Whittle-Matérn random fields
U. Khristenko
L. Scarabosio
P. Swierczynski
E. Ullmann
B. Wohlmuth
144
47
0
20 Sep 2018
Goal-oriented optimal approximations of Bayesian linear inverse problems
Goal-oriented optimal approximations of Bayesian linear inverse problemsSIAM Journal on Scientific Computing (SISC), 2016
Alessio Spantini
Tiangang Cui
Karen E. Willcox
L. Tenorio
Youssef Marzouk
193
36
0
07 Jul 2016
A randomized maximum a posterior method for posterior sampling of high
  dimensional nonlinear Bayesian inverse problems
A randomized maximum a posterior method for posterior sampling of high dimensional nonlinear Bayesian inverse problems
Kainan Wang
T. Bui-Thanh
Omar Ghattas
147
51
0
11 Feb 2016
1
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