Scientific Machine Learning, emerging topics

Contribution List

26. Registration

6/18/24, 1:30 PM

27. Opening

6/18/24, 2:15 PM

1. Deciphering Thrombosis: Advancing Multi-Scale Modeling with THRONE

Marco Laudato (KTH Royal Institute of Technology)

6/18/24, 2:30 PM

4. SOGA: Second Order Gaussian Approximation of Probabilistic Programs

Francesca Randone (University of Trieste)

6/18/24, 3:30 PM

3. Operator Learning for Ionic Models in Biomathematics

Edoardo Centofanti (Università degli Studi di Pavia)

6/18/24, 4:00 PM

2. DeepONet-based inverse approximator in matrix-free applications

Caterina Millevoi (University of Padova)

6/18/24, 5:00 PM

5. Turbulence Subgrid Closure for the Lattice Boltzmann Method via Artificial Neural Networks

Giulio Ortali (Eindhoven University of Technology, SISSA International School for Advanced Studies)

6/18/24, 5:30 PM

6. Domain Decomposition and Machine Learning - Two Mutually Beneficial Areas

Axel Klawonn (Universitaet zu Koeln)

6/19/24, 9:30 AM

7. A Reduced Order Approach for Artificial Neural Networks applied to Object Recognition

Laura Meneghetti (SISSA)

6/19/24, 11:00 AM

9. Deep learning approaches for meshless surrogate models in variable geometries

Stefano Pagani (MOX-Dipartimento di Matematica, Politecnico di Milano)

6/19/24, 11:30 AM

11. Deep unfolding for matrix factorizations

Erik Chinellato (Università degli Studi di Padova)

6/19/24, 12:00 PM

13. Delta-PINNs: physics-informed neural networks on complex geometries

Simone Pezzuto (Università di Trento)

6/19/24, 2:30 PM

14. Graph-based machine learning approaches for model order reduction

Federico Pichi (SISSA)

6/19/24, 3:30 PM

16. Learning stability on graphs

Antonioreneè Barletta (Spici srl)

6/19/24, 4:00 PM

17. Poster Session

6/19/24, 5:00 PM

Accepted posters:

Andrea Bonfanti
Matteo Tomasetto
Pavan Pranjivan Mehta
Taniya Kapoor
Jonas Actor
Guglielmo Padula
Johannes Müller
Kateryna Morozovska
Federica Bragone
Jonas Nießen
Francesco Giacomarra
Sajad Salavatidezfouli
Kabir Bakhshaei
Luca Pellegrini
Ivan Cucchi

19. Rate estimates of ONets

Christoph Schwab (SAM ETH)

6/20/24, 9:30 AM

20. Latent Dynamics Networks (LDNets): learning the intrinsic dynamics of spatio-temporal processes

Francesco Regazzoni (MOX, Dipartimento di Matematica, Politecnico di Milano)

6/20/24, 11:00 AM

22. Mesh-informed reduced order models for aneurysm risk prediction

Giuseppe Alessio D'inverno (SISSA)

6/20/24, 11:30 AM

24. Parameterization learning for scattered data adaptive spline fitting

Sofia Imperatore (University of Florence)

6/20/24, 12:00 PM

23. Solving the inverse problem in complex systems via machine learning: challenges and perspectives

Constantinos Siettos (University of Naples Federico II)

6/20/24, 2:30 PM

21. PINA: a PyTorch Framework for Solving Differential Equations with Deep Learning

Dario Coscia (SISSA)

6/20/24, 3:30 PM

18. Pre-trained physics-informed deep learning-based reduced order models for nonlinear parametrized PDEs in small data regimes

Simone Brivio (Politecnico di Milano)

6/20/24, 4:00 PM

25. Panel Session

6/20/24, 5:00 PM

15. Data-driven Inference of Chemical Reaction Networks via Graph-based Variational Autoencoders

Francesca Cairoli (Università di Trieste)

6/21/24, 9:30 AM

12. Discovery of Dirichlet-to-Neumann Maps on Graphs via Gaussian Processes

Adrienne Propp (Stanford University)

6/21/24, 11:00 AM

10. Unfolding the Kalman Filter iterations in a machine learning framework

Fabio Marcuzzi (Università degli Studi di Padova)

6/21/24, 11:30 AM

8. A comparative review of regression techniques for predicting the physical characteristics of ship engines

Fatemeh Mohammadizadehsorouei (SISSA)

6/21/24, 12:00 PM