Bridging scales: At the crossroads among renormalisation group, multi-scale modelling, and deep learning

Contribution List

1. Registration

15/04/2024, 08:30

29. Welcome

15/04/2024, 09:30

30. Topological polymeric soft materials: Some examples

Luca Tubiana (University of Trento, Italy)

15/04/2024, 09:50

32. Renormalisation group techniques for polymers on fractal lattices

Antonio Trovato (University of Padova - Department of Physic and Astronomy)

15/04/2024, 11:30

31. Coupling DFT, SSCHA and SchNet NN for Hydrogen desorption temperature calculations in Magnesium Hydride Nanoclusters

Andrea Pedrielli (FBK)

15/04/2024, 14:30

6. ML and diffentiable programming optimization for X-ray experiments

Fabrizio Napolitano (INFN - Laboratori Nazionali di Frascati, Italy)

15/04/2024, 15:00

7. From atoms to devices: Designing materials for future devices

Sanghamitra Neogi (University of Colorado Boulder)

15/04/2024, 16:00

8. Fundamental limits of Shallow Neural Networks for Supervised Learning

Jean Barbier (ICTP)

16/04/2024, 09:00

9. Modelling complex data with RBMs

Beatriz Seoane Bartolome (LISN, Paris-Saclay)

16/04/2024, 09:50

10. Renormalizing Molecular Dynamics

Pietro Faccioli (University of Milan-Bicocca)

16/04/2024, 11:30

11. Natural swarms in 3.99 dimensions

Mattia Scandolo (ISC-CNR)

16/04/2024, 14:30

12. A quantum machine learning perspective on phase transitions

Oriel Kiss (CERN)

16/04/2024, 15:00

13. Operator Leaning Renormalization Group for Quantum Simulations

Di Luo (Massachusetts Institute of Technology)

16/04/2024, 16:00

14. Machine learning in biomolecular simulations: from characterizing conformational free energy landscapes to scale bridging

Christine Peter (University of Konstanz)

17/04/2024, 09:00

15. What do Transformers learn when trained via Masked Language Modelling?

Federica Gerace

17/04/2024, 09:50

16. Inferring phase transitions and critical exponents from limited observations with Thermodynamic Maps

Pratyush Tiwary (University of Maryland, College Park)

17/04/2024, 11:30

18. Loss is more: exploring the weight space of a perceptron via enhanced sampling techniques

Margherita Mele (University of Trento)

17/04/2024, 15:00

19. Diffusion Model as Stochastic Quantization in Lattice QFT

Kai Zhou (Frankfurt Institute for Advanced Studies)

17/04/2024, 16:00

20. A computation-dissipation tradeoff for machine learning at the mesoscale

Alessandro Ingrosso (The Abdus Salam International Centre for Theoretical Physics)

18/04/2024, 09:00

21. Exploring the landscape of model representations

Will Noid (penn state)

18/04/2024, 09:50

22. Neural Quantum States for Quantum Many-Body Physics

Arnau Rios Huguet (Institute of Cosmos Sciences, University of Barcelona, Spain)

18/04/2024, 11:30

23. Thrustworthy machine learning for materials

Federico Grasselli (EPFL)

18/04/2024, 14:30

24. Tracking and Analysis of Active Droplet Dynamics

Matteo Scandola (University of Trento)

18/04/2024, 15:00

25. Transferable coarse-grained models accelerate chemical-space exploration

Tristan Bereau (Heidelberg University)

18/04/2024, 16:00

33. TBA

Yannick Meurice (University of Iowa)

18/04/2024, 16:50

26. Fixed point actions from lattice gauge equivariant convolutional neural networks

Andreas Ipp (TU Wien)

19/04/2024, 09:00

27. Towards multi-scale models of active filaments

Emanuele Locatelli (University of Padova)

19/04/2024, 09:50

28. An integrated photonic neuromorphic interface for dimensionality expansion of optical signals

Alessio Lugnan (University of Trento)

19/04/2024, 11:30