SYNERGIZE: Synergizing Numerical Methods and Machine Learning for a new generation of computational models

The SYNERGIZE project, funded by FIS (Fondo Italiano per la Scienza), seeks to advance the emerging field of Scientific Machine Learning, in which Machine Learning techniques are harmonized with Scientific Computing methods to address pressing challenges in modeling natural, social, and industrial processes. The project focuses on the development of machine-learning-based surrogate models and data-driven components of physical models, tailored to address the complexities of Scientific Computing problems. The methods developed through SYNERGIZE are expected to provide fast approximations of the solutions of differential problems, significantly reducing the need for repeated high-fidelity simulations and their associated computational demands and environmental impact.