Name
Abstract | ||
|---|---|---|
Data science models, although successful in a number of commercial domains, have had limited applicability in scientific problems involving complex physical phenomena. Theory-guided data science (TGDS) is an emerging paradigm that aims to leverage the wealth of scientific knowledge for improving the effectiveness of data science models in enabling scientific discovery. The overarching vision of TG... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TKDE.2017.2720168 | IEEE Transactions on Knowledge and Data Engineering |
Keywords | Field | DocType |
Data science,Data models,Biological system modeling,Mathematical model,Knowledge discovery,Atmospheric modeling,Numerical models | Data science,Data mining,Interpretability,Data modeling,Scientific discovery,Domain knowledge,Sociology of scientific knowledge,Computer science,Discovery science,Scientific theory,Knowledge extraction,Management science | Journal |
Volume | Issue | ISSN |
29 | 10 | 1041-4347 |
Citations | PageRank | References |
28 | 1.27 | 24 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anuj Karpatne | 1 | 109 | 16.77 |
| Gowtham Atluri | 2 | 96 | 8.09 |
| James H. Faghmous | 3 | 67 | 6.52 |
| Michael Steinbach | 4 | 1760 | 91.22 |
| Arindam Banerjee | 5 | 4716 | 233.98 |
| Auroop R. Ganguly | 6 | 286 | 29.53 |
| Shashi Shekhar | 7 | 4352 | 1098.43 |
| Nagiza F. Samatova | 8 | 861 | 74.04 |
| Vipin Kumar | 9 | 11560 | 934.35 |