Sebastian Johann Wetzel
Sebastian Johann Wetzel
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Unsupervised learning of phase transitions: from principal component analysis to variational autoencoders
SJ Wetzel
arXiv preprint arXiv:1703.02435, 2017
Machine learning of explicit order parameters: From the Ising model to SU (2) lattice gauge theory
SJ Wetzel, M Scherzer
Physical Review B 96 (18), 184410, 2017
Physics and the choice of regulators in functional renormalisation group flows
JM Pawlowski, MM Scherer, R Schmidt, SJ Wetzel
Annals of Physics 384, 165-197, 2017
Discovering symmetry invariants and conserved quantities by interpreting siamese neural networks
SJ Wetzel, RG Melko, J Scott, M Panju, V Ganesh
Physical Review Research 2 (3), 033499, 2020
Spectral reconstruction with deep neural networks
L Kades, JM Pawlowski, A Rothkopf, M Scherzer, JM Urban, SJ Wetzel, ...
Physical Review D 102 (9), 096001, 2020
Twin Neural Network Regression
SJ Wetzel, K Ryczko, RG Melko, I Tamblyn
arXiv preprint arXiv:2012.14873, 2020
Logic guided genetic algorithms
D Ashok, J Scott, S Wetzel, M Panju, V Ganesh
arXiv preprint arXiv:2010.11328, 2020
Exploring the hubbard model on the square lattice at zero temperature with a bosonized functional renormalization approach
SJ Wetzel
arXiv preprint arXiv:1712.04297, 2017
Logic Guided Genetic Algorithms (Student Abstract)
D Ashok, J Scott, SJ Wetzel, M Panju, V Ganesh
Proceedings of the AAAI Conference on Artificial Intelligence 35 (18), 15753 …, 2021
Orbital-Free Density Functional Theory with Small Datasets and Deep Learning
K Ryczko, SJ Wetzel, RG Melko, I Tamblyn
arXiv preprint arXiv:2104.05408, 2021
Exploring Phase Diagrams with Functional Renormalization and Artificial Neural Networks: From the Hubbard Model to Lattice Gauge Theory
SJ Wetzel
Twin Neural Network Regression is a Semi-Supervised Regression Algorithm
SJ Wetzel, RG Melko, I Tamblyn
arXiv preprint arXiv:2106.06124, 2021
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