Machine learning-based surrogate models are important to model complex systems at a reduced computational cost; however, they must often be re-evaluated and adapted for validity on future data. Diaw and colleagues propose an online training method leveraging optimizer-directed sampling to produce surrogate models that can be applied to any future data and demonstrate the approach on a dense nuclear-matter equation of state containing a phase transition.
- A. Diaw
- M. McKerns
- M. S. Murillo