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DTSTART:20201109T160000Z
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TRANSP:OPAQUE
LOCATION:ICFO Auditorium and Online (Teams)
SUMMARY:ICFO | GORKA MUÑOZ GIL
CLASS:PUBLIC
DESCRIPTION:Diffusion refers to numerous phenomena\, by which particles and
  bodies of all kinds move throughout any kind of material\, has emerged as
  one of the most prominent subjects in the study of complex systems. Motiv
 ated by the recent developments in experimental techniques\, the field had
  an important burst in theoretical research\, particularly in the study of
  the motion of particles in biological environments. Just with the informa
 tion retrieved from the trajectories of particles we are now able to chara
 cterize many properties of the system with astonishing accuracy. For insta
 nce\, when Einstein introduced the diffusion theory back in 1905\, he used
  the motion of microscopic particles to calculate the size of the atoms of
  the liquid these were suspended. Initially\, most of the experimental evi
 dence showed that such systems follow Brownian-like dynamics\, i.e. the ho
 mogeneous interaction between the particles and the environment led to its
  stochastic\, but uncorrelated motion. However\, we know now that such a s
 imple explanation lacks crucial phenomena that have been shown to arise in
  a plethora of physical systems. The divergence from Brownian dynamics led
  to the theory of anomalous diffusion\, in which the particles are affecte
 d in a way or another by their interactions with the environment such that
  their diffusion changes drastically. For instance features such as ergodi
 city\, Gaussianity\, or ageing are now crucial for in the understanding of
  diffusion processes\, well beyond Brownian motion.\nIn theoretical terms\
 , anomalous diffusion has a well-developed framework\, able to explain mos
 t of the current experimental observations. However\, it has been usually 
 focused in describing the systems in terms of its macroscopic behaviour. T
 his means that the processes are described by means of general models\, ab
 le to predict the average or collective features. Even though such an appr
 oach leads to a correct description of the system and hints on the actual 
 underlying phenomena\, it lacks the understanding of the particular micros
 copic interactions leading to anomalous diffusion.\nThe work presented in 
 this Thesis has two main goals. First\, we will explore how one may use mi
 croscopical (or phenomenological) models to understand anomalous diffusion
 . By microscopical model we refer to a model in which we will set exactly 
 how the interactions between the various components of a system are. Then\
 , we will explore how these interactions may be tuned in order to recover 
 and control anomalous diffusion and how its features depend on the propert
 ies of the system. We will explore crucial topics arising in recent experi
 mental observations\, such as weak-ergodicity breaking or liquid-liquid ph
 ase separation. Second\, we will survey the topic of trajectory characteri
 zation. Even if our theories are extremely well developed\, without an acc
 urate tool for studying the trajectories observed in experiments\, we will
  be unable to correctly make any faithful prediction. In particular\, we w
 ill introduce one of the first machine learning techniques that can be use
 d for such purpose\, even in systems where previous techniques failed larg
 ely.\nMonday November 9\, 17:00\, MsTeams - Auditorium\nThesis Advisor: Pr
 of Dr Maciej Lewenstein\nThesis Co-advisor: Dr Miguel Angel Garcia-March
DTSTAMP:20260429T174923Z
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