Stefania Fresca

Stefania Fresca

Assistant Professor

Department of Mathematics, Politecnico di Milano

Biography

Stefania Fresca is Assistant Professor in Numerical Analysis at MOX (Laboratory for Modeling and Scientific Computing) - Department of Mathematics, Politecnico di Milano, Italy, within the Future Artificial Intelligence Research (FAIR) Project.
After carrying out her PhD in the framework of the ERC Advanced Grant Project iHEART (PI: Prof. Alfio Quarteroni) devoted to cardiac modeling, she spent two years as Post-Doctoral Research Fellow at MOX.
Her research interests and expertise include scientific machine learning, reduced order modeling (data dimensionality reduction), deep learning, digital twins, and numerical approximation of PDEs, with several applications to engineering problems.

Interests
  • Scientific machine learning
  • Reduced order modeling
  • AI
Education
  • PhD in Mathematical Models and Methods in Engineering, 2021

    Politecnico di Milano

  • MSc in Mathematical Engineering - Computational Science and Engineering, 2017

    Politecnico di Milano - Université Pierre et Marie Curie (Sorbonne Universités)

  • BSc in Mathematical Engineering, 2014

    Politecnico di Milano

Experience

 
 
 
 
 
Future Artificial Intelligence Research (FAIR) Foundation
Assistant Professor
February 2023 – Present Milano, Italy
 
 
 
 
 
Participation to the program “The mathematical and statistical foundation of future data-driven engineering”
 
 
 
 
 
Ernst & Young
Risk Advisory Intern
June 2017 – November 2017 Milano, Italy

Responsibilities include:

  • Design and data modelling of a Datamart
  • Data extraction activities through SQL
  • Automation of Data Quality processes through Access and VBA
 
 
 
 
 
Université Pierre et Marie Curie (Sorbonne Universités)
Exchange Program
September 2015 – March 2016 Paris, France

Publications

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(2023). Deep Learning-based surrogate models for parametrized PDEs: handling geometric variability through graph neural networks. Chaos: An Interdisciplinary Journal of Nonlinear Science, 33(12): 12312..

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(2023). Efficient approximation of cardiac mechanics through reduced order modeling with deep learning-based operator approximation. International Journal for Numerical Methods in Biomedical Engineering, e3783.

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(2023). Uncertainty quantification for nonlinear solid mechanics using reduced order models with Gaussian process regression. Computers and Mathematics with Applications, 149, 1-23.

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(2023). Long-time prediction of nonlinear parametrized dynamical systems by deep learning-based reduced order models. Mathematics in Engineering, 5(6):1-36.

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(2023). Modeling the periodic response of Micro-Electromechanical Systems through deep learning-based approaches. Actuators, 12, 278.

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Media & Communications

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Post in Coventor MEMS+ Blog
Using Machine Learning to Develop a Real-Time Model of a MEMS Disk Resonating Gyroscope.
Post in Coventor MEMS+ Blog
Seminar @ Machine Learning + X Seminars (CRUNCH Group, Brown University)
Deep learning-based reduced order models for parametrized PDEs.
Seminar @ Machine Learning + X Seminars (CRUNCH Group, Brown University)
Article in Enginsoft Newsletter - RESEARCH & INNOVATION
Deep learning-based reduced order models - the new frontier in numerical simulation for microsystems.
Article in Enginsoft Newsletter - RESEARCH & INNOVATION
Talk @ Mathematics of Deep Learning Workshop (Isaac Newton Institute, University of Cambridge)
POD-DL-ROM - a comprehensive deep learning-based approach to reduced order modeling of nonlinear time-dependent parametrized PDEs.
Talk @ Mathematics of Deep Learning Workshop (Isaac Newton Institute, University of Cambridge)
Talk @ MCF2021 Congress
Deep learning-based reduced order models for the real-time approximation of nonlinear time-dependent parametrized PDEs.
Talk @ MCF2021 Congress
Talk @ 36th international CAE conference and exhibition
How medicine and engineering interrelate - a female bioengineering perspective.
Talk @ 36th international CAE conference and exhibition
Interview for iHEART project channel
How will artificial intelligence contribute to computational cardiac medicine of the future.
Interview for iHEART project channel
Interview ioDONNA magazine
A PhD student studies how to cure the heart with mathematics.
Interview ioDONNA magazine

Contacts

Feel free to contact me!
Reach out to me with scientific opportunities and deep learning projects.