Multiscale Sampling for the Inverse Modeling of Partial Differential Equations-Reference-Cited by-同舟云学术

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Multiscale Sampling for the Inverse Modeling of Partial Differential Equations

Published:2023-11 Issue: Volume: Page:112609
ISSN:0021-9991
Container-title:Journal of Computational Physics
language:en
Short-container-title:Journal of Computational Physics

Author:

Ali Alsadig^ORCID,Al-Mamun Abdullah,Pereira Felipe^ORCID,Rahunanthan Arunasalam

Funder

National Institute of Food and Agriculture

Central State University

U.S. Department of Agriculture

Publisher

Elsevier BV

Subject

Computer Science Applications,Physics and Astronomy (miscellaneous),Applied Mathematics,Computational Mathematics,Modeling and Simulation,Numerical Analysis

Reference46 articles.

1. N. Metropolis, S. Ulam, The Monte Carlo method, Journal of the American statistical association 44 (1949) 335–341.

2. N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, E. Teller, Equation of state calculations by fast computing machines, The journal of chemical physics 21 (1953) 1087–1092.

3. W. K. Hastings, Monte Carlo sampling methods using Markov chains and their applications, Biometrika 57 (1970) 97–109.

4. X. T. Tong, M. Morzfeld, Y. M. Marzouk, MALA-within-Gibbs samplers for high-dimensional distributions with sparse conditional structure, SIAM Journal on Scientific Computing 42 (2020) A1765–A1788.

5. S. Brooks, A. Gelman, G. Jones, X.-L. Meng, Handbook of Markov chain Monte Carlo, CRC press, 2011.

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