496 citations · 883 across the 12 of their papers we have counts for
14 papers
Autonomous phase discovery
Shiyu Zhou, Yuxuan Zhang, Sebastian Wetzel +2
Understanding quantum phases of matter has long relied on physicists' intuition and mathematical tools such as symmetry and topology. Remarkably successful as these approaches have…
How to get the most out of Twinned Regression Methods
Sebastian J. Wetzel
Twinned regression methods are designed to solve the dual problem to the original regression problem, predicting differences between regression targets rather then the targets them…
Unsupervised Learning of Rydberg Atom Array Phase Diagram with Siamese Neural Networks
Zakaria Patel, Ejaaz Merali, Sebastian J. Wetzel
We introduce an unsupervised machine learning method based on Siamese Neural Networks (SNN) to detect phase boundaries. This method is applied to Monte-Carlo simulations of Ising-t…
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…
Twin Neural Network Regression is a Semi-Supervised Regression Algorithm
Sebastian J. Wetzel, Roger G. Melko, Isaac Tamblyn
Twin neural network regression (TNNR) is a semi-supervised regression algorithm, it can be trained on unlabelled data points as long as other, labelled anchor data points, are pres…
Toward Orbital-Free Density Functional Theory with Small Data Sets and Deep Learning
Kevin Ryczko, Sebastian J. Wetzel, Roger G. Melko +1
We use voxel deep neural networks to predict energy densities and functional derivatives of electron kinetic energies for the Thomas-Fermi model and Kohn-Sham density functional th…
Twin Neural Network Regression
Sebastian J. Wetzel, Kevin Ryczko, Roger G. Melko +1
We introduce twin neural network (TNN) regression. This method predicts differences between the target values of two different data points rather than the targets themselves. The s…
Logic Guided Genetic Algorithms
Dhananjay Ashok, Joseph Scott, Sebastian Wetzel +2
We present a novel Auxiliary Truth enhanced Genetic Algorithm (GA) that uses logical or mathematical constraints as a means of data augmentation as well as to compute loss (in conj…
Discovering Symmetry Invariants and Conserved Quantities by Interpreting Siamese Neural Networks
Sebastian J. Wetzel, Roger G. Melko, Joseph Scott +2
In this paper, we introduce interpretable Siamese Neural Networks (SNN) for similarity detection to the field of theoretical physics. More precisely, we apply SNNs to events in spe…
Spectral Reconstruction with Deep Neural Networks
Lukas Kades, Jan M. Pawlowski, Alexander Rothkopf +5
We explore artificial neural networks as a tool for the reconstruction of spectral functions from imaginary time Green's functions, a classic ill-conditioned inverse problem. Our a…
Exploring the Hubbard Model on the Square Lattice at Zero Temperature with a Bosonized Functional Renormalization Approach
Sebastian Johann Wetzel
We employ the functional renormalization group to investigate the phase diagram of the Hubbard model on the square lattice with finite chemical potential at zero tempera…
Machine Learning of Explicit Order Parameters: From the Ising Model to SU(2) Lattice Gauge Theory
Sebastian Johann Wetzel, Manuel Scherzer
We present a procedure for reconstructing the decision function of an artificial neural network as a simple function of the input, provided the decision function is sufficiently sy…
Unsupervised learning of phase transitions: from principal component analysis to variational autoencoders
Sebastian Johann Wetzel
We employ unsupervised machine learning techniques to learn latent parameters which best describe states of the two-dimensional Ising model and the three-dimensional XY model. Thes…
Physics and the choice of regulators in functional renormalisation group flows
Jan M. Pawlowski, Michael M. Scherer, Richard Schmidt +1
The Renormalisation Group is a versatile tool for the study of many systems where scale-dependent behaviour is important. Its functional formulation can be cast into the form of an…