Conferences - Seminars
Computational mathematics for model reduction and predictive modelling in molecular and complex systems
By Markos Katsoulakis, University of Massachusetts Amherst Benedikt Leimkuhler, University of Edinburgh Petr Pelchac, University of Delaware Anders Szepessy, KTH Royal Institute of Technology
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.
21 to 24 May 2019 - CIB premises (Room GA 3 21).
27 to 29 May 2019 - CECAM premises (Room BCH 3113).
Part of the Semester : Multi-scale Mathematical Modelling and Coarse-grain Computational Chemistry
Organization John Maddocks, EPFL - Christof Schütte, Freie Universität Berlin - Carsten Hartmann, BTU Cottbus-Senftenberg
Accessibility General public