4 citations · 5 across the 7 of their papers we have counts for
7 papers
Entropy production of active matter systems as indicator for computing performance
Patrick Egenlauf, Hannes A. Kröninger, Arnulf Kung +2
Physical systems can process information through their natural dynamics, offering alternatives to conventional digital computing. Reservoir computing offers a basic framework by us…
Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model
Max Weinmann, Miriam Klopotek
We study how unsupervised autoencoders trained on microscopic spin configurations from the Ising model learn macroscopic, theory-relevant variables underlying the data-generating p…
Capturing reduced-order quantum many-body dynamics out of equilibrium via neural ordinary differential equations
Patrick Egenlauf, Iva Březinová, Sabine Andergassen +1
Out-of-equilibrium quantum many-body systems exhibit rapid correlation buildup that underlies many emerging phenomena. Exact wave-function methods to describe this scale exponentia…
Optimal information injection and transfer mechanisms for active matter reservoir computing
Mario U. Gaimann, Miriam Klopotek
Reservoir computing (RC) is a state-of-the-art machine learning method that makes use of the power of dynamical systems (the reservoir) for real-time inference. When using biologic…
Robustly optimal dynamics for active matter reservoir computing
Mario U. Gaimann, Miriam Klopotek
Information processing abilities of active matter are studied in the reservoir computing (RC) paradigm to infer the future state of a chaotic signal. We uncover an exceptional regi…
Interpretable Machine Learning in Physics: A Review
Sebastian Johann Wetzel, Seungwoong Ha, Raban Iten +2
Machine learning is increasingly transforming various scientific fields, enabled by advancements in computational power and access to large data sets from experiments and simulatio…
Machine learning stochastic differential equations for the evolution of order parameters of classical many-body systems in and out of equilibrium
Francesco Carnazza, Federico Carollo, Sabine Andergassen +3
We develop a machine learning algorithm to infer the emergent stochastic equation governing the evolution of an order parameter of a many-body system. We train our neural network t…