Machine learning for active matter

The availability of large datasets has boosted the application of machine learning in many fields and is now starting to shape active-matter research as well. Machine learning techniques have already been successfully applied to active-matter data—for example, deep neural networks to analyse images...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Nature machine intelligence Ročník 2; číslo 2; s. 94 - 103
Hlavní autoři: Cichos, Frank, Gustavsson, Kristian, Mehlig, Bernhard, Volpe, Giovanni
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 01.02.2020
Nature Publishing Group
Témata:
631/114/2398
639/301
639/705
Algorithms
Artificial neural networks
Bacteria
Behavior
Biofilms
Birds
Complexity
Computer and Information Sciences
Computer Science
Data- och informationsvetenskap (Datateknik)
deep
Energy
Engineering
Equilibrium
Genetic code
Machine learning
Microorganisms
Microscopy
Noise
Phase transitions
phytoplankton
Review Article
Robots
strategy
ISSN:2522-5839, 2522-5839
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:The availability of large datasets has boosted the application of machine learning in many fields and is now starting to shape active-matter research as well. Machine learning techniques have already been successfully applied to active-matter data—for example, deep neural networks to analyse images and track objects, and recurrent nets and random forests to analyse time series. Yet machine learning can also help to disentangle the complexity of biological active matter, helping, for example, to establish a relation between genetic code and emergent bacterial behaviour, to find navigation strategies in complex environments, and to map physical cues to animal behaviours. In this Review, we highlight the current state of the art in the application of machine learning to active matter and discuss opportunities and challenges that are emerging. We also emphasize how active matter and machine learning can work together for mutual benefit. This Review surveys machine learning techniques that are currently developed for a range of research topics in biological and artificial active matter and also discusses challenges and exciting opportunities. This research direction promises to help disentangle the complexity of active matter and gain fundamental insights for instance in collective behaviour of systems at many length scales from colonies of bacteria to animal flocks.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2522-5839
2522-5839
DOI:10.1038/s42256-020-0146-9