Name: Scientific Machine Learning, emerging topics
Start: 2024-06-18T13:30:00+02:00
End: 2024-06-21T13:00:00+02:00
Location: SISSA, International School for Advanced Studies, main campus, Trieste, Italy

Scientific Machine Learning, emerging topics

from Tuesday, June 18, 2024 (1:30 PM) to Friday, June 21, 2024 (1:00 PM)

Monday, June 17, 2024 ¶
Tuesday, June 18, 2024 ¶

1:30 PM Registration
Registration
1:30 PM - 2:15 PM
Room: 128-129

2:15 PM Opening
Opening
2:15 PM - 2:30 PM
Room: 128-129
2:30 PM Deciphering Thrombosis: Advancing Multi-Scale Modeling with THRONE - Marco Laudato (KTH Royal Institute of Technology)
Deciphering Thrombosis: Advancing Multi-Scale Modeling with THRONE
- Marco Laudato (KTH Royal Institute of Technology)
2:30 PM - 3:30 PM
Room: 128-129
3:30 PM SOGA: Second Order Gaussian Approximation of Probabilistic Programs - Francesca Randone (University of Trieste)
SOGA: Second Order Gaussian Approximation of Probabilistic Programs
- Francesca Randone (University of Trieste)
3:30 PM - 4:00 PM
Room: 128-129
4:00 PM Operator Learning for Ionic Models in Biomathematics - Edoardo Centofanti (Università degli Studi di Pavia)
Operator Learning for Ionic Models in Biomathematics
- Edoardo Centofanti (Università degli Studi di Pavia)
4:00 PM - 4:30 PM
Room: 128-129
4:30 PM Coffee break
Coffee break
4:30 PM - 5:00 PM
Room: 128-129
5:00 PM DeepONet-based inverse approximator in matrix-free applications - Caterina Millevoi (University of Padova)
DeepONet-based inverse approximator in matrix-free applications
- Caterina Millevoi (University of Padova)
5:00 PM - 5:30 PM
Room: 128-129
5:30 PM Turbulence Subgrid Closure for the Lattice Boltzmann Method via Artificial Neural Networks - Giulio Ortali (Eindhoven University of Technology, SISSA International School for Advanced Studies)
Turbulence Subgrid Closure for the Lattice Boltzmann Method via Artificial Neural Networks
- Giulio Ortali (Eindhoven University of Technology, SISSA International School for Advanced Studies)
5:30 PM - 6:00 PM
Room: 128-129
Wednesday, June 19, 2024 ¶
9:30 AM Domain Decomposition and Machine Learning - Two Mutually Beneficial Areas - Axel Klawonn (Universitaet zu Koeln)
Domain Decomposition and Machine Learning - Two Mutually Beneficial Areas
- Axel Klawonn (Universitaet zu Koeln)
9:30 AM - 10:30 AM
Room: 128-129
10:30 AM Coffee Break
Coffee Break
10:30 AM - 11:00 AM
Room: 128-129
11:00 AM A Reduced Order Approach for Artificial Neural Networks applied to Object Recognition - Laura Meneghetti (SISSA)
A Reduced Order Approach for Artificial Neural Networks applied to Object Recognition
- Laura Meneghetti (SISSA)
11:00 AM - 11:30 AM
Room: 128-129
11:30 AM Deep learning approaches for meshless surrogate models in variable geometries - Stefano Pagani (MOX-Dipartimento di Matematica, Politecnico di Milano)
Deep learning approaches for meshless surrogate models in variable geometries
- Stefano Pagani (MOX-Dipartimento di Matematica, Politecnico di Milano)
11:30 AM - 12:00 PM
Room: 128-129
12:00 PM Deep unfolding for matrix factorizations - Erik Chinellato (Università degli Studi di Padova)
Deep unfolding for matrix factorizations
- Erik Chinellato (Università degli Studi di Padova)
12:00 PM - 12:30 PM
Room: 128-129
12:30 PM Lunch break
Lunch break
12:30 PM - 2:30 PM
Room: 128-129
2:30 PM Delta-PINNs: physics-informed neural networks on complex geometries - Simone Pezzuto (Università di Trento)
Delta-PINNs: physics-informed neural networks on complex geometries
- Simone Pezzuto (Università di Trento)
2:30 PM - 3:30 PM
Room: 128-129
3:30 PM Graph-based machine learning approaches for model order reduction - Federico Pichi (SISSA)
Graph-based machine learning approaches for model order reduction
- Federico Pichi (SISSA)
3:30 PM - 4:00 PM
Room: 128-129
4:00 PM Learning stability on graphs - Antonioreneè Barletta (Spici srl)
Learning stability on graphs
- Antonioreneè Barletta (Spici srl)
4:00 PM - 4:30 PM
Room: 128-129
4:30 PM Coffee Break
Coffee Break
4:30 PM - 5:00 PM
Room: 128-129

5:00 PM Poster Session
Poster Session
5:00 PM - 7:00 PM
Room: 128-129 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
Thursday, June 20, 2024 ¶
9:30 AM Rate estimates of ONets - Christoph Schwab (SAM ETH)
Rate estimates of ONets
- Christoph Schwab (SAM ETH)
9:30 AM - 10:30 AM
Room: 128-129
10:30 AM Coffee Break
Coffee Break
10:30 AM - 11:00 AM
Room: 128-129
11:00 AM Latent Dynamics Networks (LDNets): learning the intrinsic dynamics of spatio-temporal processes - Francesco Regazzoni (MOX, Dipartimento di Matematica, Politecnico di Milano)
Latent Dynamics Networks (LDNets): learning the intrinsic dynamics of spatio-temporal processes
- Francesco Regazzoni (MOX, Dipartimento di Matematica, Politecnico di Milano)
11:00 AM - 11:30 AM
Room: 128-129
11:30 AM Mesh-informed reduced order models for aneurysm risk prediction - Giuseppe Alessio D'inverno (SISSA)
Mesh-informed reduced order models for aneurysm risk prediction
- Giuseppe Alessio D'inverno (SISSA)
11:30 AM - 12:00 PM
Room: 128-129
12:00 PM Parameterization learning for scattered data adaptive spline fitting - Sofia Imperatore (University of Florence)
Parameterization learning for scattered data adaptive spline fitting
- Sofia Imperatore (University of Florence)
12:00 PM - 12:30 PM
Room: 128-129
12:30 PM Lunch Break
Lunch Break
12:30 PM - 2:30 PM
Room: 128-129
2:30 PM Solving the inverse problem in complex systems via machine learning: challenges and perspectives - Constantinos Siettos (University of Naples Federico II)
Solving the inverse problem in complex systems via machine learning: challenges and perspectives
- Constantinos Siettos (University of Naples Federico II)
2:30 PM - 3:30 PM
Room: 128-129
3:30 PM PINA: a PyTorch Framework for Solving Differential Equations with Deep Learning - Dario Coscia (SISSA)
PINA: a PyTorch Framework for Solving Differential Equations with Deep Learning
- Dario Coscia (SISSA)
3:30 PM - 4:00 PM
Room: 128-129
4:00 PM Pre-trained physics-informed deep learning-based reduced order models for nonlinear parametrized PDEs in small data regimes - Simone Brivio (Politecnico di Milano)
Pre-trained physics-informed deep learning-based reduced order models for nonlinear parametrized PDEs in small data regimes
- Simone Brivio (Politecnico di Milano)
4:00 PM - 4:30 PM
Room: 128-129
4:30 PM Coffee Break
Coffee Break
4:30 PM - 5:00 PM
Room: 128-129

5:00 PM Panel Session
Panel Session
5:00 PM - 6:00 PM
Room: 128-129

8:00 PM Social Dinner
Social Dinner
8:00 PM - 10:00 PM
Room: 128-129
Friday, June 21, 2024 ¶
9:30 AM Data-driven Inference of Chemical Reaction Networks via Graph-based Variational Autoencoders - Francesca Cairoli (Università di Trieste)
Data-driven Inference of Chemical Reaction Networks via Graph-based Variational Autoencoders
- Francesca Cairoli (Università di Trieste)
9:30 AM - 10:30 AM
Room: 128-129
10:30 AM Coffee break
Coffee break
10:30 AM - 11:00 AM
Room: 128-129
11:00 AM Discovery of Dirichlet-to-Neumann Maps on Graphs via Gaussian Processes - Adrienne Propp (Stanford University)
Discovery of Dirichlet-to-Neumann Maps on Graphs via Gaussian Processes
- Adrienne Propp (Stanford University)
11:00 AM - 11:30 AM
Room: 128-129
11:30 AM Unfolding the Kalman Filter iterations in a machine learning framework - Fabio Marcuzzi (Università degli Studi di Padova)
Unfolding the Kalman Filter iterations in a machine learning framework
- Fabio Marcuzzi (Università degli Studi di Padova)
11:30 AM - 12:00 PM
Room: 128-129
12:00 PM A comparative review of regression techniques for predicting the physical characteristics of ship engines - Fatemeh Mohammadizadehsorouei (SISSA)
A comparative review of regression techniques for predicting the physical characteristics of ship engines
- Fatemeh Mohammadizadehsorouei (SISSA)
12:00 PM - 12:30 PM
Room: 128-129
12:30 PM Closing
Closing
12:30 PM - 1:00 PM
Room: 128-129

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