Data-driven Problem Identification
Data-Driven Problem Identification is a systematic approach to recognizing and formulating problems by analyzing empirical data, distinguished from intuition- or theory-driven methods through its reliance on observable patterns and quantitative evidence to detect anomalies, gaps, or inefficiencies requiring investigation or intervention.
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1.7K
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126
Authors
86
Institutions
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2017–2026
29
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40
Walid M. Abdelmoula, Benjamin Balluff, Sonja Englert et al. · Proceedings of the National Academy of Sciences · 2016 · 185 citations · Full text
Medicine, Biostatistics, Oncology +17
Data-driven discovery of PDEs in complex datasets
Jens Berg, Kaj Nyström · Journal of Computational Physics · 2019 · 159 citations · Full text
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Maastricht University
Maastricht, The Netherlands
3 h-index
6 publications
278 citations
Carnegie Mellon University
Pittsburgh, United States
2 h-index
3 publications
82 citations
University of California, Irvine
Irvine, United States
2 h-index
3 publications
202 citations
Tallinn University of Technology
Tallinn, Estonia
1 h-index
2 publications
26 citations
University of Virginia
Charlottesville, United States
1 h-index
1 publications
37 citations

Universidad Autónoma de Madrid
Madrid, Spain
1 h-index
1 publications
34 citations
Mercy Hospital
St Louis, United States
1 h-index
1 publications
21 citations

Institut Gustave Roussy
Villejuif, France
1 h-index
1 publications
22 citations
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4 publications · 157 citations
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1 publications · 34 citations
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1 publications · 23 citations
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CSR · 1 publications · 23 citations
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Repository · 1 publications · 21 citations
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