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Sebastian Johann Wetzel
Sebastian Johann Wetzel
Verified email at perimeterinstitute.ca
Title
Cited by
Cited by
Year
Unsupervised learning of phase transitions: from principal component analysis to variational autoencoders
SJ Wetzel
arXiv preprint arXiv:1703.02435, 2017
5192017
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
1632017
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
862020
Modern applications of machine learning in quantum sciences
A Dawid, J Arnold, B Requena, A Gresch, M Płodzień, K Donatella, ...
arXiv preprint arXiv:2204.04198, 2022
752022
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
732017
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
652020
Toward orbital-free density functional theory with small data sets and deep learning
K Ryczko, SJ Wetzel, RG Melko, I Tamblyn
Journal of Chemical Theory and Computation 18 (2), 1122-1128, 2022
352022
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
262021
Twin neural network regression is a semi-supervised regression algorithm
SJ Wetzel, RG Melko, I Tamblyn
Machine Learning: Science and Technology 3 (4), 045007, 2022
142022
Twin neural network regression
SJ Wetzel, K Ryczko, RG Melko, I Tamblyn
Applied AI Letters 3 (4), e78, 2022
122022
Unsupervised learning of Rydberg atom array phase diagram with Siamese neural networks
Z Patel, E Merali, SJ Wetzel
New Journal of Physics 24 (11), 113021, 2022
92022
Modern applications of machine learning in quantum sciences. 2022. doi: 10.48550
A Dawid, J Arnold, B Requena, A Gresch, M Płodzień, K Donatella, ...
arXiv preprint ARXIV.2204.04198, 0
9
Closed-Form Interpretation of Neural Network Classifiers with Symbolic Regression Gradients
SJ Wetzel
arXiv preprint arXiv:2401.04978, 2024
32024
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
32017
Twin neural network improved k-nearest neighbor regression
SJ Wetzel
International Journal of Data Science and Analytics, 1-11, 2024
12024
How to get the most out of Twinned Regression Methods
SJ Wetzel
arXiv preprint arXiv:2301.01383, 2023
12023
Exploring Phase Diagrams with Functional Renormalization and Artificial Neural Networks: From the Hubbard Model to Lattice Gauge Theory
SJ Wetzel
12018
Closed-Form Interpretation of Neural Network Latent Spaces with Symbolic Gradients
Z Patel, SJ Wetzel
arXiv preprint arXiv:2409.05305, 2024
2024
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