Designing accurate emulators for scientific processes using calibration-driven deep models
oleh: Jayaraman J. Thiagarajan, Bindya Venkatesh, Rushil Anirudh, Peer-Timo Bremer, Jim Gaffney, Gemma Anderson, Brian Spears
| Format: | Article |
|---|---|
| Diterbitkan: | Nature Portfolio 2020-11-01 |
Deskripsi
The success of machine learning for scientific discovery normally depends on how well the inherent assumptions match the problem in hand. Here, Thiagarajan et al. alleviate this constraint by allowing the change of optimization criterion in a data-driven approach to emulate complex scientific processes.