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Computational mathematics for model reduction and predictive modelling in molecular and complex systems
mai 21 - mai 29
The workshop will address computational and theoretical issues in stochastic modeling and model reduction in molecular and complex systems. In particular, the program will include topics ranging from quantum to mesoscale modelling, with focus on uncertainty quantification, machine learning and approximate inference method, methods using both quantum and classical models, semiclassical limits and their computational aspect. Furthermore, the interplay between mathematical analysis, modeling and statistical physics and trade-offs between statistical (data-driven) learning and physicochemical modelling will be part of the discussions. The workshop aims at bringing together mathematicians and domain scientists interested in applications such as systems with excited states, empirical potentials, kinetic Monte Carlo methods and accelerated simulation methods determined from molecular dynamics, data assimilation and inference for predictive modeling of complex molecular systems.