Surrogate modeling and physics-informed learning for complex systems - 2025_IDR_DMAT_7
Deadline: 03/02/2026
Scientific-Disciplinary Group
01/MATH-05 - Numerical Analysis
Description
The research program aims to develop advanced methodologies at the interface between Numerical Analysis and Machine Learning, with a focus on physics-informed machine learning. The goal is to design learning strategies that embed the structure of governing physical laws, enabling robust inference even when data are scarce or uncertain. The project will also explore surrogate modeling approaches for the rapid approximation of solutions to complex differential problems. These models will serve as efficient and physically consistent proxies for traditional solvers, supporting tasks such as forward simulations, uncertainty quantification, and data assimilation. Project funded by Ministero dell’Università e della Ricerca in the context of FIS 2, SYNERGIZE, code FIS-2023-02228, CUP D53C24005440001.
Job posting website
Number of positions
1
Funding body
Politecnico di Milano
View the original posting on the MUR website: Go to MUR website