Nicola Demo

Research Fellow, SISSA - International School for Advanced Studies

Nicola Demo is a researcher and co-founder of Fast Computing Srl. With a focus on reduced order modeling, data-driven modeling, and machine learning, he has made significant contributions to these fields during his tenure as a Researcher Fellow at SISSA mathLab. His research extends to data-driven modeling, where he leverages machine learning algorithms and statistical methods to extract valuable insights from extensive datasets. Moreover, he actively collaborates on industrial projects, employing his computational expertise to tackle real-world challenges. Through his publications, industrial partnerships, and role as CTO in the Fast Computing startup, he continues to advance computational methodologies, fostering the integration of academia and industry for practical applications.


Deep Learning Approaches for Solving Differential Equations: Physics-Informed Neural Network and Neural Operator


Differential equation learning is an emerging field that aims at computing the solution of differential equations through neural network models. This presentation explores state-of-the-art methodologies such as physics-informed neural networks and neural operators applied to ordinary and partial differential equations. We will begin with an in-depth overview of these techniques, highlighting their strengths and limitations. Following this, we will introduce PINA, a powerful Python library that simplifies the implementation and manipulation of these advanced methods. A significant portion of the presentation will be dedicated to demonstrating the usage of PINA, including an interactive session where participants will solve simple differential equation problems in real-time. This hands-on experience will illustrate how these approaches can be applied to various scientific and engineering challenges, enhancing the accuracy and efficiency of mathematical modeling.


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