Federico Pichi

Federico Pichi is an Assistant Professor (RtdA) at the International School for Advanced Studies (SISSA) in Trieste, Italy, where he is a key member of the mathLab research group. His work focuses on developing computational frameworks for studying parametrized partial differential equations, with particular emphasis on systems exhibiting bifurcating behavior and multiple solution branches.

Dr. Pichi's academic foundation includes:

• PhD in Mathematical Analysis, Modelling and Applications from SISSA (2016-2020)
• Master's degree in Mathematics from Sapienza University of Rome (2014-2016)
• Bachelor's degree in Mathematics from Sapienza University of Rome (2011-2014)

His research expertise spans Numerical Analysis, Mathematical Modeling, and Scientific Computing, with specialization in reduced order modeling techniques for nonlinear parametrized PDEs. Dr. Pichi has made significant contributions to understanding bifurcating phenomena in computational fluid dynamics, particularly the Coanda effect in channels. His methodological approach uniquely bridges traditional projection-based reduced order models with cutting-edge machine learning techniques, including artificial neural networks and graph convolutional autoencoders, to address the computational challenges of nonlinear systems with multiple solution branches.

Analysis of Dr. Pichi's recent publication trajectory reveals an increasingly sophisticated integration of optimal transport theory with deep learning approaches to tackle slow-decaying Kolmogorov n-width problems in reduced order modeling. His work demonstrates remarkable versatility across application domains, from cardiovascular flow modeling to hyperelastic material analysis, while maintaining a consistent methodological focus on efficient computational frameworks for bifurcating systems. The incorporation of graph-based neural networks for resolution-invariant operator learning represents his most recent methodological innovation.

As an active member of the mathLab research group under Professor Gianluigi Rozza, Dr. Pichi contributes to a collaborative environment that bridges theoretical mathematics with practical engineering applications. His research has established important connections between classical numerical analysis and modern machine learning paradigms, making significant contributions to the field of scientific computing for complex nonlinear systems.