Gert Aarts

According to our database, Gert Aarts authored at least 21 papers between 2020 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Strategic White Paper on AI Infrastructure for Particle, Nuclear, and Astroparticle Physics: Insights from JENA and EuCAIF.

Sascha Caron, Andreas Ipp, Gert Aarts, Gábor Bíró, Daniele Bonacorsi, Elena Cuoco, Caterina Doglioni, Tommaso Dorigo, Julián García Pardiñas, Stefano Giagu, Tobias Golling, Lukas Heinrich, Ik Siong Heng, Paula Gina Isar, Karolos Potamianos, Liliana Teodorescu, John Veitch, Pietro Vischia, Christoph Weniger

CoRR, March, 2025

Physics-Conditioned Diffusion Models for Lattice Gauge Theory.

Qianteng Zhu, Gert Aarts, Wei Wang, Kai Zhou, Lingxiao Wang

CoRR, February, 2025

Physics-Driven Learning for Inverse Problems in Quantum Chromodynamics.

Gert Aarts, Kenji Fukushima, Tetsuo Hatsuda, Andreas Ipp, Shuzhe Shi, Lingxiao Wang, Kai Zhou

CoRR, January, 2025

On learning higher-order cumulants in diffusion models.

Gert Aarts, Diaa E. Habibi, Lingxiao Wang, Kai Zhou

Mach. Learn. Sci. Technol., 2025

2024

DiaaEddinH/On-learning-higher-order-cumulants-in-diffusion-models: v1.0.2.

Diaa E. Habibi, Gert Aarts, Lingxiao Wang, Kai Zhou

Dataset, October, 2024

DiaaEddinH/On-learning-higher-order-cumulants-in-diffusion-models: v1.0.1.

Diaa Eddin Habibi, Gert Aarts, Lingxiao Wang, Kai Zhou

Dataset, October, 2024

Random Matrix Theory for Stochastic Gradient Descent.

Chanju Park, Matteo Favoni, Biagio Lucini, Gert Aarts

CoRR, 2024

Diffusion models learn distributions generated by complex Langevin dynamics.

Diaa E. Habibi, Gert Aarts, Lingxiao Wang, Kai Zhou

CoRR, 2024

Dyson Brownian motion and random matrix dynamics of weight matrices during learning.

Gert Aarts, Ouraman Hajizadeh, Biagio Lucini, Chanju Park

CoRR, 2024

Stochastic weight matrix dynamics during learning and Dyson Brownian motion.

Gert Aarts, Biagio Lucini, Chanju Park

CoRR, 2024

2023

Generative Diffusion Models for Lattice Field Theory.

Lingxiao Wang, Gert Aarts, Kai Zhou

CoRR, 2023

Diffusion Models as Stochastic Quantization in Lattice Field Theory.

Lingxiao Wang, Gert Aarts, Kai Zhou

CoRR, 2023

2022

Applications of Machine Learning to Lattice Quantum Field Theory.

Denis Boyda, Salvatore Calì, Sam Foreman, Lena Funcke, Daniel C. Hackett, Yin Lin, Gert Aarts, Andrei Alexandru, Xiao-Yong Jin, Biagio Lucini, Phiala E. Shanahan

CoRR, 2022

2021

Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy.

Yogesh K. Dwivedi, David Laurie Hughes, Elvira Ismagilova, Gert Aarts, Crispin R. Coombs, Tom Crick, Yanqing Duan, Rohita Dwivedi, John S. Edwards, Aled Eirug, Vassilis Galanos, P. Vigneswara Ilavarasan, Marijn Janssen, Paul Jones, Arpan Kumar Kar, Hatice Kizgin, Bianca Kronemann, Banita Lal, Michael D. Williams

Int. J. Inf. Manag., 2021

Towards a Shapley Value Graph Framework for Medical peer-influence.

Jamie Andrew Duell, Monika Seisenberger, Gert Aarts, Shang-Ming Zhou, Xiuyi Fan

CoRR, 2021

Quantum field theories, Markov random fields and machine learning.

Dimitrios Bachtis, Gert Aarts, Biagio Lucini

CoRR, 2021

Machine learning with quantum field theories.

Dimitrios Bachtis, Gert Aarts, Biagio Lucini

CoRR, 2021

Quantum field-theoretic machine learning.

Dimitrios Bachtis, Gert Aarts, Biagio Lucini

CoRR, 2021

A Comparison of Explanations Given by Explainable Artificial Intelligence Methods on Analysing Electronic Health Records.

Jamie Andrew Duell, Xiuyi Fan, Bruce Burnett, Gert Aarts, Shang-Ming Zhou

Proceedings of the IEEE EMBS International Conference on Biomedical and Health Informatics, 2021

2020

Adding machine learning within Hamiltonians: Renormalization group transformations, symmetry breaking and restoration.

Dimitrios Bachtis, Gert Aarts, Biagio Lucini

CoRR, 2020

Extending machine learning classification capabilities with histogram reweighting.

Dimitrios Bachtis, Gert Aarts, Biagio Lucini

CoRR, 2020