AI Research
Lecture notes on Physics Informed Neural Networks, Neural Operators, and their applications
Bridging physical principles with deep learning architectures for scientific computing.
This is the set of lecture notes for the PhD course \href{https://www.unibz.it/en/faculties/engineering/phd-computer-science/study-course-offering/2025/36967}{\textit{Physics Informed Neural Network}, held at the University of Bozen/Bolzano} in the academic year 2025/2026. The go...
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ArXiv CS.LG
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ArXiv CS.LG
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Physics-informed neural networks bridge the gap between pure data-driven modeling and established scientific laws by embedding differential equations into training. This hybrid approach significantly improves model reliability and accuracy in fields like fluid dynamics and structural engineering where data scarcity is common.
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