Stochastic upscaling in solid mechanics: An excercise in machine learning-Reference-Cited by-同舟云学术

Stochastic upscaling in solid mechanics: An excercise in machine learning

Published:2007-09 Issue:1 Volume:226 Page:301-325
ISSN:0021-9991
Container-title:Journal of Computational Physics
language:en
Short-container-title:Journal of Computational Physics

Author:

Koutsourelakis P.S.

Publisher

Elsevier BV

Subject

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

Reference31 articles.

1. The mathematical theory of equilibrium of cracks in brittle fracture;Barenblatt;Advances in Applied Mechanics,1962

2. Asymptotic analysis for Periodic Structures;Bensoussan,1978

3. Rate Distortion Theory: A Mathematical Basis for Data Compression;Berger,1971

4. Computation of channel capacity and rate–distortion functions;Blahut;IEEE Transactions on Information Theory,1972

5. Computational modeling of impact damage in brittle materials;Camacho;International Journal of Solids and Structures,1996

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