122 citations · 488 across the 17 of their papers we have counts for
18 papers
Unsupervised and probabilistic learning with Contrastive Local Learning Networks: The Restricted Kirchhoff Machine
Marcelo Guzman, Simone Ciarella, Andrea J. Liu
Autonomous physical learning systems modify their internal parameters and solve computational tasks without relying on external computation. Compared to traditional computers, they…
Shape matters: Inferring the motility of confluent cells from static images
Quirine J. S. Braat, Giulia Janzen, Bas C. Jansen +3
Cell motility in dense cell collectives is pivotal in various diseases like cancer metastasis and asthma. A central aspect in these phenomena is the heterogeneity in cell motility,…
Classifying the age of a glass based on structural properties: A machine learning approach
Giulia Janzen, Casper Smit, Samantha Visbeek +5
It is well established that physical aging of amorphous solids is governed by a marked change in dynamical properties as the material becomes older. Conversely, structural properti…
Dead or alive: Distinguishing active from passive particles using supervised learning
Giulia Janzen, Xander L. J. A. Smeets, Vincent E. Debets +4
A longstanding open question in the field of dense disordered matter is how precisely structure and dynamics are related to each other. With the advent of machine learning, it has…
Dynamics of supercooled liquids from static averaged quantities using machine learning
Simone Ciarella, Massimiliano Chiappini, Emanuele Boattini +2
We introduce a machine-learning approach to predict the complex non-Markovian dynamics of supercooled liquids from static averaged quantities. Compared to techniques based on parti…
Finding defects in glasses through machine learning
Simone Ciarella, Dmytro Khomenko, Ludovic Berthier +4
Structural defects control the kinetic, thermodynamic and mechanical properties of glasses. For instance, rare quantum tunneling two-level systems (TLS) govern the physics of glass…