dc.contributor.author
Sbailò, Luigi
dc.date.accessioned
2020-03-04T13:09:40Z
dc.date.available
2020-03-04T13:09:40Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/26830
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-26588
dc.description.abstract
This thesis deals with the development and formalization of algorithms designed for an efficient simulation of biological systems. This work is separated into two different parts, and in each part a different algorithm is investigated. In the first part of the thesis, an algorithm that is used to simulate biological systems at the mesoscopic scale is outlined. The aforementioned algorithm is studied in detail, and several improvements, theoretical, algorithmic and technical, are presented. In the second part of the thesis, a novel sampling method is outlined, which uses deep-learning to accelerate the computation of equilibrium properties of systems defined with atomistic detail. The two parts lead to applications at different scales, and, in the future, methods and concepts developed in this thesis can be useful for the investigation of biological processes defined with mesoscopic or microscopic detail.
en
dc.format.extent
xiv, 120 Seiten
dc.rights.uri
http://www.fu-berlin.de/sites/refubium/rechtliches/Nutzungsbedingungen
dc.subject
statistical sampling
en
dc.subject
efficient simulations
en
dc.subject
Langevin equation
en
dc.subject
Markov chain Monte Carlo
en
dc.subject
deep learning
en
dc.subject.ddc
500 Natural sciences and mathematics::510 Mathematics::519 Probabilities and applied mathematics
dc.title
Efficient multi-scale sampling methods in statistical physics
dc.contributor.gender
male
dc.contributor.firstReferee
Noé, Frank
dc.contributor.furtherReferee
Kleist, Max von
dc.date.accepted
2019-12-19
dc.identifier.urn
urn:nbn:de:kobv:188-refubium-26830-5
refubium.affiliation
Mathematik und Informatik
dcterms.accessRights.dnb
free
dcterms.accessRights.openaire
open access