Characterizing the Dynamics of Active Matter using Graph Neural Networks
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Complex adaptive systems (MPCAS), MSc
Publicerad
2023
Författare
RUTGERSSON, CHRISTIAN
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Biological systems often have self-organizing properties. On the microscopic scale,
the self-organization may be driven by constituents that generate their own motility
by expending energy. Systems made up of such constituents, that self-propel, are
termed active matter. Via this definition, it is clear that it is important to un derstand the rules that govern the microscopic constituents of an active matter in
order to understand the active matter itself. Even though active matter is a topic
of interest today, finding good methods of qualitatively and quantitatively charac terizing the constituents of active matter remains an issue. Coincidentally, different
types of artificial neural networks (ANN) have, in recent years, been used increas ingly in research settings with great success. One such network is called the graph
neural network (GNN). As the name suggests, this network is specifically designed
to work with graphs as input data. Graphs can act as a useful representation of a
system of particles, including active matter systems. Therefore, this project aims
to characterize the forces that underpin an active matter system consisting of inter acting particles that also have an active component, using a special type of GNN
called message passing network (MPN). This was done by creating a rudimentary
simulation of this type of active matter, and training an MPN on it using standard
machine learning algorithms. In the end, the simulations were found to give rise to
characteristic active matter phenomena, and the MPN was able to correctly predict
the force dynamics of a particle in the given active matter system.
Beskrivning
Ämne/nyckelord
active matter, particle system simulation, machine learning, neural net works, graph neural networks, message passing network, message passing.