Physics-Informed Optimization is a computational optimization framework that incorporates fundamental physical laws, typically expressed as partial differential equations or conservation principles, as inductive biases or constraints within the objective function or solution space to guide the search toward physically consistent and interpretable designs in inverse design problems.
645
Publications
46K
Citations
2K
Authors
653
Institutions
Publications per year
2017–2026
621
Parents
Publications
645
RIME: A physics-based optimization
Hang Su, Dong Zhao, Ali Asghar Heidari et al. · Neurocomputing · 2023 · 983 citations
1–5 of 645
Authors
2K
- GE
21
h-index
- YG
10
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- JB
9
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- AD
8
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- HJ
8
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1–5 of 2K
Institutions
653
Brown University
Providence, United States
22 h-index
50 publications
15.8K citations
Massachusetts Institute of Technology
Cambridge, United States
18 h-index
39 publications
8.2K citations
University of Illinois Urbana-Champaign
Urbana, United States
17 h-index
50 publications
3.9K citations
Tsinghua University
Beijing, China
16 h-index
40 publications
2.2K citations
Centre National de la Recherche Scientifique
Paris, France
15 h-index
33 publications
1.2K citations
Queensland University of Technology
Brisbane, Australia
12 h-index
57 publications
3.9K citations
Pacific Northwest National Laboratory
Richland, United States
11 h-index
21 publications
4.6K citations
Pennsylvania State University
State College, United States
11 h-index
25 publications
1.6K citations
1–8 of 653
Venues
- Computer Methods in Applied Mechanics and Engineering
47 publications · 8.6K citations
33
h-index
- Journal of Computational Physics
27 publications · 3.7K citations
23
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- arXiv (Cornell University)
Repository · 31 publications · 1.3K citations
17
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- Engineering Applications of Artificial Intelligence
15 publications · 1.3K citations
15
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- Mechanical Systems and Signal Processing
MSSP · 10 publications · 678 citations
11
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