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hIPPYlib-MUQ: A Bayesian Inference Software Framework for Integration of
  Data with Complex Predictive Models under Uncertainty
v1v2 (latest)

hIPPYlib-MUQ: A Bayesian Inference Software Framework for Integration of Data with Complex Predictive Models under Uncertainty

1 December 2021
Ki-tae Kim
Umberto Villa
M. Parno
Youssef Marzouk
Omar Ghattas
N. Petra
ArXiv (abs)PDFHTMLGithub

Papers citing "hIPPYlib-MUQ: A Bayesian Inference Software Framework for Integration of Data with Complex Predictive Models under Uncertainty"

7 / 7 papers shown
Non-parametric Inference for Diffusion Processes: A Computational
  Approach via Bayesian Inversion for PDEs
Non-parametric Inference for Diffusion Processes: A Computational Approach via Bayesian Inversion for PDEs
Maximilian Kruse
Sebastian Krumscheid
284
0
0
04 Nov 2024
Distribution learning via neural differential equations: a nonparametric
  statistical perspective
Distribution learning via neural differential equations: a nonparametric statistical perspectiveJournal of machine learning research (JMLR), 2023
Youssef Marzouk
Zhi Ren
Sven Wang
Jakob Zech
279
22
0
03 Sep 2023
Principal Feature Detection via $Φ$-Sobolev Inequalities
Principal Feature Detection via ΦΦΦ-Sobolev InequalitiesBernoulli (Bernoulli), 2023
Matthew T.C. Li
Youssef Marzouk
O. Zahm
281
15
0
10 May 2023
Infinite-Dimensional Diffusion Models
Infinite-Dimensional Diffusion Models
Jakiw Pidstrigach
Youssef Marzouk
Sebastian Reich
Sven Wang
618
32
0
20 Feb 2023
Further analysis of multilevel Stein variational gradient descent with
  an application to the Bayesian inference of glacier ice models
Further analysis of multilevel Stein variational gradient descent with an application to the Bayesian inference of glacier ice modelsAdvances in Computational Mathematics (ACM), 2022
Terrence Alsup
Tucker Hartland
Benjamin Peherstorfer
N. Petra
337
1
0
06 Dec 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
403
61
0
21 Jun 2022
A gradient-free subspace-adjusting ensemble sampler for
  infinite-dimensional Bayesian inverse problems
A gradient-free subspace-adjusting ensemble sampler for infinite-dimensional Bayesian inverse problems
Matthew M. Dunlop
G. Stadler
BDL
225
6
0
22 Feb 2022
1
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