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Dates: September 22–23, 2025
Location: Sokolovská 83, 186 75 Praha 8, Campus Karlín (room K1)
Keywords: Solid-state NMR, MAS, CP MAS, simulation (SIMPSON), optimization, time-dependent ODEs, high-performance computing, pulse sequence design
Description:
In this two days workshop we want to bring together different areas of scientific computing, bridging applications with theoretical and data analysis. Specifically, we will discuss challenging problems arising from NMR such as spin dynamic simulation, optimal control, and spectral analysis and the use advance numerical and AI techniques for solving them.
This workshop is part of a series of event that is establishing a network among EU4+ universities (Charles University, Sorbonne University, and Warsaw University) under the SEEDS4EU+ funded project INGREDIENT (https://www.karlin.mff.cuni.cz/~pozza/ingredient). The workshop will be the occasion to discuss the future advancement of this collaboration.
Organizers
Zdeněk Tošner
Stefano Pozza
Program and information
program_prague_eu4.pdfVenue:
Karlín campus of Charles University — Sokolovská 83, Praha 8, room K1.
From Václav Havel Airport
Take trolleybus 59 from outside Terminal 1 or 2 toward Nádraží Veleslavín (Metro A).
At Nádraží Veleslavín, board Metro Line A (green line), direction towards Depo Hostivař / city center.
Change from Line A to Line B (yellow line) at Můstek.
Take Metro Line B to Křižíkova station.
The venue is about 150 meters from Křižíkova station.
From Prague Main Train Station (Hlavní nádraží)
From Hlavní nádraží, take Metro Line C (red line) to Florenc station.
At Florenc, transfer to Metro Line B (yellow line), direction Černý Most.
Ride Line B to Křižíkova station.
Walk ~ 150 meters from Křižíkova station to the Karlín campus building.
Materials
Materials
INGREDIENT Bonhomme FINAL 2 2025.pdfC. Bonhomme, Concrete examples of Path-Sum for NMR: Could ML play a decisive role?
SEED4EU_Prague_2025_pozza.pdfS. Pozza, Solution of Ordinary and Fractional Differential Equations via the ⋆-Approach
P. Balaž, Introduction to Machine Learning in Physics
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