27 citations · 119 across the 12 of their papers we have counts for
12 papers
Machine learning the Ising transition: A comparison between discriminative and generative approaches
Difei Zhang, Frank Schäfer, Julian Arnold
The detection of phase transitions is a central task in many-body physics. To automate this process, the task can be phrased as a classification problem. Classification problems ca…
Phase Transitions in the Output Distribution of Large Language Models
Julian Arnold, Flemming Holtorf, Frank Schäfer +1
In a physical system, changing parameters such as temperature can induce a phase transition: an abrupt change from one state of matter to another. Analogous phenomena have recently…
Machine learning phase transitions: Connections to the Fisher information
Julian Arnold, Niels Lörch, Flemming Holtorf +1
Despite the widespread use and success of machine-learning techniques for detecting phase transitions from data, their working principle and fundamental limits remain elusive. Here…
Fast Detection of Phase Transitions with Multi-Task Learning-by-Confusion
Julian Arnold, Frank Schäfer, Niels Lörch
Machine learning has been successfully used to study phase transitions. One of the most popular approaches to identifying critical points from data without prior knowledge of the u…
Mapping out phase diagrams with generative classifiers
Julian Arnold, Frank Schäfer, Alan Edelman +1
One of the central tasks in many-body physics is the determination of phase diagrams. However, mapping out a phase diagram generally requires a great deal of human intuition and un…
Performance Bounds for Quantum Feedback Control
Flemming Holtorf, Frank Schäfer, Julian Arnold +2
The limits of quantum feedback control have immediate consequences for quantum information science at large, yet remain largely unexplored. Here, we combine quantum filtering theor…
Combining Machine Learning and Spectroscopy to Model Reactive Atom + Diatom Collisions
Juan Carlos San Vicente Veliz, Julian Arnold, Raymond J. Bemish +1
The prediction of product translational, vibrational, and rotational energy distributions for arbitrary initial conditions for reactive atom+diatom collisions is of considerable pr…
Modern applications of machine learning in quantum sciences
Anna Dawid, Julian Arnold, Borja Requena +26
In this book, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learn…
Replacing neural networks by optimal analytical predictors for the detection of phase transitions
Julian Arnold, Frank Schäfer
Identifying phase transitions and classifying phases of matter is central to understanding the properties and behavior of a broad range of material systems. In recent years, machin…
Machine Learning Product State Distributions from Initial Reactant States for a Reactive Atom-Diatom Collision System
Julian Arnold, Juan Carlos San Vicente Veliz, Debasish Koner +3
A machine learned (ML) model for predicting product state distributions from specific initial states (state-to-distribution or STD) for reactive atom-diatom collisions is presented…
Interpretable and unsupervised phase classification
Julian Arnold, Frank Schäfer, Martin Žonda +1
Fully automated classification methods that yield direct physical insights into phase diagrams are of current interest. Here, we demonstrate an unsupervised machine learning method…
Machine Learning for Observables: Reactant to Product State Distributions for Atom-Diatom Collisions
Julian Arnold, Debasish Koner, Silvan Käser +3
Machine learning-based models to predict product state distributions from a distribution of reactant conditions for atom-diatom collisions are presented and quantitatively tested.…