Dian Wu

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Giuseppe CarleoAssociate Professor of Physics - EPFL, Lausanne, SwitzerlandVerified email at epfl.ch
Filippo VicentiniAssistant Professor in Quantum Physics and AI, Ecole Polytechnique, ParisVerified email at polytechnique.edu
Pan ZhangInstitute of Theoretical Physics, Chinese Academy of SciencesVerified email at itp.ac.cn
Lei WangInstitute of Physics, Chinese Academy of SciencesVerified email at iphy.ac.cn
Damian HofmannVerified email at uni-hamburg.de
Nikita AstrakhantsevGoogle Quantum AIVerified email at google.com
Vladimir Vargas-CalderónD-Wave Quantum Systems Inc.Verified email at unal.edu.co
Gabriel PesciaPhD Student Physics, École Polytechnique Fédérale de Lausanne, EPFLVerified email at epfl.ch
Riccardo RossiLPTMC, CNRS, Sorbonne Université, Paris, FranceVerified email at cnrs.fr
Attila SzabóAmbizione fellow, University of ZürichVerified email at physik.uzh.ch
Jannes NysETH ZürichVerified email at phys.ethz.ch
Christopher RothPostdoctoral Research FellowVerified email at flatironinstitute.org
Yubei ChenUC Davis | Aizip.aiVerified email at ucdavis.edu
Bruno OlshausenProfessor, UC BerkeleyVerified email at berkeley.edu
Hong-Ye Hu (扈鸿业)HQI Postdoc Fellow, Harvard Quantum Initiative, Harvard UniversityVerified email at fas.harvard.edu

Dian Wu

Other names吴典

PhD student at EPFL CQSL

Verified email at epfl.ch

computational physics many-body systems variational Monte Carlo machine learning


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Solving Statistical Mechanics Using Variational Autoregressive Networks D Wu, L Wang, P Zhang Physical Review Letters 122 (8), 080602, 2019	262	2019
NetKet 3: Machine learning toolbox for many-body quantum systems F Vicentini, D Hofmann, A Szabó, D Wu, C Roth, C Giuliani, G Pescia, ... SciPost Physics Codebases, 007, 2022	111	2022
Variational benchmarks for quantum many-body problems D Wu, R Rossi, F Vicentini, N Astrakhantsev, F Becca, X Cao, ... Science 386 (6719), 296-301, 2024	51	2024
Unbiased Monte Carlo cluster updates with autoregressive neural networks D Wu, R Rossi, G Carleo Physical Review Research 3 (4), L042024, 2021	45	2021
From tensor-network quantum states to tensorial recurrent neural networks D Wu, R Rossi, F Vicentini, G Carleo Physical Review Research 5 (3), L032001, 2023	40	2023
RG-Flow: A hierarchical and explainable flow model based on renormalization group and sparse prior HY Hu, D Wu, YZ You, B Olshausen, Y Chen Machine Learning: Science and Technology 3 (3), 035009, 2022	18	2022
Sparse autoregressive neural networks for classical spin systems I Biazzo, D Wu, G Carleo Machine Learning: Science and Technology 5 (2), 025074, 2024	6	2024
A machine learning strategy for enhancing the strength and toughness in metal matrix composites Z Zhong, J An, D Wu, N Gao, L Liu, Z Wang, F Meng, X Zhou, T Fan International Journal of Mechanical Sciences 281, 109550, 2024	5	2024
Unveiling nonmagnetic phase and many-body entanglement in two-dimensional random quantum magnets SrCuTeWO D Wu, F Yang, G Carleo arXiv preprint arXiv:2407.05917, 2024	1	2024
Learning eigenstates of quantum many-body Hamiltonians within the symmetric subspaces using neural network quantum states ST Bao, D Wu, P Zhang, L Wang arXiv preprint arXiv:2407.20065, 2024		2024

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