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Uncertainty Quantification for Forward and Inverse Problems of PDEs via Latent Global Evolution

Uncertainty Quantification for Forward and Inverse Problems of PDEs via Latent Global Evolution

Tailin Wu
Willie Neiswanger
Hongtao Zheng
Stefano Ermon
J. Leskovec
ArXiv (abs)PDFHTMLGithub (16★)

Papers citing "Uncertainty Quantification for Forward and Inverse Problems of PDEs via Latent Global Evolution"

4 / 4 papers shown
Title
Towards Distribution-Shift Uncertainty Estimation for Inverse Problems with Generative Priors
Towards Distribution-Shift Uncertainty Estimation for Inverse Problems with Generative Priors
Namhoon Kim
Sara Fridovich-Keil
OODUQCV
81
0
0
13 Oct 2025
A reduced-order derivative-informed neural operator for subsurface fluid-flow
A reduced-order derivative-informed neural operator for subsurface fluid-flow
Jeongjin
Park
Grant Bruer
Huseyin Tuna Erdinc
A. Gahlot
Felix J. Herrmann
AI4CE
40
0
0
17 Sep 2025
HJ-sampler: A Bayesian sampler for inverse problems of a stochastic process by leveraging Hamilton-Jacobi PDEs and score-based generative models
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
179
3
0
15 Sep 2024
Active Learning for Neural PDE Solvers
Active Learning for Neural PDE SolversInternational Conference on Learning Representations (ICLR), 2024
Daniel Musekamp
Marimuthu Kalimuthu
David Holzmüller
Makoto Takamoto
Carlos Fernandez
AI4CE
289
12
0
02 Aug 2024