Daniele Apiletti

Steering Committee - Theory-Guided & Knowledge-Informed AI, Physics-Informed Scientific Computing, AI for Industrial Intelligence

Daniele’s research interests are centered on Scientific AI. His research addresses the design and application of Theory-Guided & Knowledge-Informed AI, moving beyond purely data-driven models. By embedding domain knowledge and first principles into algorithms, AI model robustness and interpretability are improved, such as in Physics-Informed Scientific Computing, where models are constrained by fundamental physical laws.

A primary application domain is AI for Industrial Intelligence. By translating these advanced AI methodologies into practical solutions for manufacturing and complex engineering systems, more reliable, efficient, and transparent predictive models are developed. Such research effectively bridges the gap between theoretical AI and applied industrial science, driving innovation in intelligent solutions and data-driven operational excellence.

Daniele has been involved in innovation development in many funded research projects and with private companies. He is co-founder of a Politecnico di Torino spin-off company. He serves as Academic Advisor of the Master’s degree in Data Science and Engineering at Politecnico di Torino, shaping the next generation of experts in this crucial field.