Surrogates for Physics-based and Data-driven Modelling of Parametric Systems: Review and New Perspectives
Surrogate models provide compact relations between user-defined input parameters and output quantities of interest, enabling the efficient evaluation of complex parametric systems in many-query settings. Such capabilities are essential in a wide range of applications, including optimisation, control, data assimilation, uncertainty quantification, and emerging digital twin technologies in various fields such as manufacturing, personalised healthcare, smart cities, and sustainability. This article reviews established methodologies for constructing surrogate models exploiting either knowledge of the governing laws and the dynamical structure of the system (physics-based) or experimental obse...
| Authors | Matteo Giacomini, Pedro Díez |
|---|---|
| Primary category | math.NA |
| Published | 13 March 2026 |
| Last updated at source | 13 March 2026 |
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