On Future Synergies for Stochastic and Learning Algorithms / Sur les synergies futures autour des algorithmes d'apprentissage et stochastiques

Collection On Future Synergies for Stochastic and Learning Algorithms / Sur les synergies futures autour des algorithmes d'apprentissage et stochastiques

Organizer(s) Durmus, Alain ; Michel, Manon ; Roberts, Gareth ; Zdeborova, Lenka

Date(s) 27/09/2021 - 01/10/2021

linked URL https://conferences.cirm-math.fr/2389.html

00:00:00 / 00:00:00

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Molecular dynamics algorithms: numerical and mathematical analysis

By Tony Lelièvre

We will present sampling algorithms which are used in computational statistical physics to sample multimodal measures and metastable dynamics. More precisely, we will focus on free energy adaptive biasing techniques to approximate thermodynamic quantities, and accelerated dynamics methods to sample the state-to-state dynamics of a metastable trajectory. The mathematical analysis of these algorithms relies on entropy techniques, and quasi-stationary distributions.

Information about the video

Date of recording 27/09/2021
Date of publication 18/11/2021
Institution CIRM
Licence CC BY NC ND
Language English
Audience Researchers
Director(s) Guillaume Hennenfent
Format MP4

Citation data

DOI 10.24350/CIRM.V.19817503
Cite this video Lelièvre, Tony (27/09/2021). Molecular dynamics algorithms: numerical and mathematical analysis. CIRM. Audiovisual resource. DOI: 10.24350/CIRM.V.19817503
URL https://dx.doi.org/10.24350/CIRM.V.19817503

Domain(s)

Bibliography

LELIEVRE, Tony et STOLTZ, Gabriel. Partial differential equations and stochastic methods in molecular dynamics. Acta Numerica, 2016, vol. 25, p. 681-880. - https://doi.org/10.1017/S0962492916000039
LELIÈVRE, Tony. Mathematical foundations of accelerated molecular dynamics methods. Handbook of Materials Modeling: Methods: Theory and Modeling, 2020, p. 773-803. - https://doi.org/10.1007/978-3-319-44677-6_27

Document(s)

https://www.cirm-math.fr/RepOrga/2389/Slides/Lelievre.pdf

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