Deep Generative Models for Simulating Physical Systems
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".
Deadline for manuscript submissions: 31 March 2026 | Viewed by 59
Special Issue Editors
Interests: machine learning; variational inference; lattice field theory; quantum computing; probabilistic inference
Interests: computational quantum physics; generative models
Special Issue Information
Dear Colleagues,
This Special Issue invites contributions at the forefront of applying Deep Generative Models (DGMs) to address fundamental challenges in physics. DGMs, including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), normalizing flows, diffusion models, and autoregressive models, have demonstrated remarkable capabilities in learning and sampling from target probability distributions in the fields of, e.g., high energy physics, statistical physics, and condensed matter physics. DGMs are rapidly transforming numerous subfields of physics that heavily rely on accurate numerical simulations and efficient sampling routines, ranging from collider physics and cosmology to quantum field theory.
We seek submissions that explore novel DGM architectures, physics-informed approaches, and innovative applications. Topics of interest include, but are not limited to, the following:
- Fast detector simulation, data augmentation, and anomaly detection in experimental physics;
- Discovering and exploring different phases of matter via DMGs;
- Generating quantum states and solving complex many-body problems (e.g., neural networks, quantum states);
- Accelerating scientific simulations (e.g., HMC) and uncertainty quantification;
- Methodological advancements: Developments of new DGM algorithms, architecture design, and novel applications;
- Incorporations of symmetries and inductive biases into DGMs.
This Special Issue aims to highlight both the successes and the outstanding challenges in integrating these powerful machine learning tools to advance our understanding of the universe.
Dr. Kim Andrea Nicoli
Prof. Dr. Lei Wang
Dr. Anindita Maiti
Guest Editors
Manuscript Submission Information
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Keywords
- generative models
- variational inference
- amortized inference
- lattice field theory
- monte Carlo methods
- normalizing flows
- autoregressive models
- statistical learning theory
- Bayesian learning
- neural networks quantum states
- enhanced sampling
- variational free energy
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