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Bayesian Calibration of Imperfect Computer Models using Physics-Informed
  Priors
v1v2v3v4 (latest)

Bayesian Calibration of Imperfect Computer Models using Physics-Informed Priors

Journal of machine learning research (JMLR), 2022
17 January 2022
Michail Spitieris
I. Steinsland
    AI4CE
ArXiv (abs)PDFHTMLGithub (5★)

Papers citing "Bayesian Calibration of Imperfect Computer Models using Physics-Informed Priors"

6 / 6 papers shown
Are Statistical Methods Obsolete in the Era of Deep Learning? A Study of ODE Inverse Problems
Are Statistical Methods Obsolete in the Era of Deep Learning? A Study of ODE Inverse Problems
Skyler Wu
Shihao Yang
S. C. Kou
AI4CE
149
0
0
27 May 2025
Multi-physics Simulation Guided Generative Diffusion Models with
  Applications in Fluid and Heat Dynamics
Multi-physics Simulation Guided Generative Diffusion Models with Applications in Fluid and Heat Dynamics
Naichen Shi
Hao Yan
Shenghan Guo
Raed Al Kontar
DiffMAI4CE
249
0
0
25 Jul 2024
Quantifying Local Model Validity using Active Learning
Quantifying Local Model Validity using Active Learning
Sven Lämmle
Can Bogoclu
Robert Voßhall
Anselm Haselhoff
Dirk Roos
271
1
0
11 Jun 2024
Gaussian processes for Bayesian inverse problems associated with linear
  partial differential equations
Gaussian processes for Bayesian inverse problems associated with linear partial differential equationsStatistics and computing (Stat. Comput.), 2023
Tianming Bai
A. Teckentrup
K. Zygalakis
299
22
0
17 Jul 2023
Parameter Inference based on Gaussian Processes Informed by Nonlinear
  Partial Differential Equations
Parameter Inference based on Gaussian Processes Informed by Nonlinear Partial Differential Equations
Zhao-Xia Li
Shih-Feng Yang
Jeff Wu
419
6
0
22 Dec 2022
Learning Physics between Digital Twins with Low-Fidelity Models and
  Physics-Informed Gaussian Processes
Learning Physics between Digital Twins with Low-Fidelity Models and Physics-Informed Gaussian Processes
Michail Spitieris
I. Steinsland
AI4CE
219
1
0
16 Jun 2022
1
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