Nonequilibrium Phenomena and AI Techniques

Dear Colleagues,

In this Special Issue, we aim to foster a rigorous, theory-driven dialog between nonequilibrium statistical mechanics and artificial intelligence (AI), emphasizing contributions that transcend heuristic applications. We welcome studies that rigorously exploit AI to analyze and model complex nonequilibrium phenomena, provided that the integration of machine learning methods is accompanied by a deep theoretical understanding of both the underlying physical system and the employed algorithms. In particular, submissions should clarify how AI contributes to the scientific insights, rather than using it as a purely computational tool.

Conversely, in this Special Issue we encourage contributions that leverage concepts from statistical physics, nonlinear dynamics, and complex networks to analyze, interpret, or improve AI models. A key challenge in modern machine learning lies in understanding the emergent behaviors of large-scale networks and energy-based models, which are often treated as “black boxes.” Nonequilibrium statistical mechanics offers principled frameworks to study learning dynamics, energy landscapes, and generalization properties, helping to render AI models more transparent and theoretically grounded.

We seek high-quality contributions exploring this bidirectional interface, including, but not limited to, AI-driven modeling of nonequilibrium processes, theoretical analyses of learning dynamics using tools from physics, physics-inspired algorithms for complex systems, and energy-based or generative approaches—such as diffusion models—where concepts from statistical mechanics and nonequilibrium physics serve as explanatory or design principles. Emphasis is placed on methodological rigor and conceptual clarity: applications of AI to physical systems must be accompanied by a solid understanding of the models’ theoretical implications, while physics-inspired AI work should clearly demonstrate how physical principles inform architecture, dynamics, or interpretability.

By stressing both directions—AI for nonequilibrium physics and nonequilibrium physics for AI—in this Special Issue we aim to establish a platform for advancing understanding at the intersection of these two vibrant and complementary fields, highlighting the role of physics not only as a source of insight but also as a guiding framework in the development of modern AI techniques.

Dr. Davide Carbone
Prof. Dr. Hong Zhao
Prof. Dr. Lamberto Rondoni
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.