Sparse Surrogate Models
Sparse Surrogate Models is a class of surrogate models that belong to the broader category of approximation models used in computational science and engineering, distinguished by their reliance on sparse representations—employing a minimal set of basis functions, samples, or features—to achieve computational efficiency and interpretability while maintaining predictive accuracy.
127
Publications
5.8K
Citations
397
Authors
213
Institutions
Publications per year
2017–2026
70
Variants
Sparse Surrogate Model
Parents
127
Polymer informatics with multi-task learning
Patterns · 2021 · 153 citations · Full text
Natural Sciences, Chemical Engineering, Materials Science +17
1–5 of 127
397
4
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University of North Carolina at Chapel Hill, United States
3 publications · 418 citations
3
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2
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European Centre for Medium-Range Weather Forecasts, United Kingdom
2 publications · 161 citations
2
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2
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1–5 of 397
213
Georgia Institute of Technology
Atlanta, United States
5 h-index
13 publications
1K citations
University of Southampton
Southampton, United Kingdom
3 h-index
7 publications
244 citations
University of North Carolina at Chapel Hill
Chapel Hill, United States
3 h-index
4 publications
435 citations

Northwestern Polytechnical University
Xi'an, China
3 h-index
8 publications
310 citations
University of Illinois Urbana-Champaign
Urbana, United States
3 h-index
6 publications
163 citations
Texas A&M University
College Station, United States
3 h-index
4 publications
189 citations

Xi'an Jiaotong University
Xi'an, China
3 h-index
8 publications
225 citations
University of Stuttgart
Stuttgart, Germany
3 h-index
5 publications
111 citations
1–8 of 213
5 publications · 180 citations
5
h-index
3 publications · 493 citations
3
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Repository · 3 publications · 127 citations
3
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3 publications · 134 citations
3
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2 publications · 35 citations
2
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1–5