Data-driven Multiscale Modeling
Data-Driven Multiscale Modeling is a computational framework within multiscale solid mechanics that integrates empirical or simulated data across spatial and temporal scales to inform, refine, or construct constitutive models or homogenization procedures, distinguishing itself from traditional physics-based approaches by relying on data-informed rather than purely first-principles-derived relationships.
67
Publications
4.4K
Citations
242
Authors
152
Institutions
Publications per year
2017–2026
47
Parents
67
Frank Losasso, Tamar Shinar, Andrew Selle et al. · ACM Transactions on Graphics · 2006 · 247 citations
Salah A. Faroughi, Nikhil M. Pawar, Célio Fernandes et al. · Journal of Computing and Information Science in Engineering · 2024 · 201 citations
Computing, Computer Science, Physics +17
1–5 of 67
242
6
h-index
4
h-index
University of Washington Applied Physics Laboratory, United States
4 publications · 647 citations
4
h-index
3
h-index
3
h-index
1–5 of 242
152
University of Washington
Seattle, United States
5 h-index
10 publications
1.8K citations
Stanford University
Stanford, United States
4 h-index
13 publications
1.6K citations
Yale University
New Haven, United States
4 h-index
7 publications
559 citations

Centre National de la Recherche Scientifique
Paris, France
3 h-index
5 publications
480 citations
University of Washington Applied Physics Laboratory
Seattle, United States
3 h-index
3 publications
213 citations
Wuhan University
Wuhan, China
3 h-index
15 publications
1.1K citations

Johns Hopkins University
Baltimore, United States
3 h-index
7 publications
392 citations
University College London
London, United Kingdom
3 h-index
3 publications
336 citations
1–8 of 152
5 publications · 347 citations
5
h-index
2 publications · 135 citations
2
h-index
2 publications · 381 citations
2
h-index
2 publications · 126 citations
2
h-index
Repository · 2 publications · 37 citations
2
h-index
1–5