Beyond Electrons: A Theoretical Framework for Near-Field Radiative Thermal Computing and Neural-Network-Inspired Processing

[2608.06203] Beyond Electrons: A Theoretical Framework for Near-Field Radiative Thermal Computing and Neural-Network-Inspired Processing Skip to main content Search Submit Donate Log in Condensed Matter > Mesoscale and Nanoscale Physics arXiv:2608.06203 (cond-mat) [Submitted on 6 Aug 2026 ( v1 ), last revised 26 Aug 2026 (this version, v2)] Title: Beyond Electrons: A Theoretical Framework for Near-Field Radiative Thermal Computing and Neural-Network-Inspired Processing Authors: Hexiang Zhang , Mauro Antezza , Yi Zheng View PDF Abstract: Near field radiative heat transfer provides a route for information processing in which thermal radiation, rather than charge transport, serves as the physical carrier of signals. Here, we propose and theoretically analyze a programmable near field radiative thermal computing framework in which radiative coupling, phase change nonlinearity, and thermal state memory are mapped onto neural network inspired operations. The framework is constructed from near field radiative thermal diodes, transistors, and multi terminal logic units separated by nanoscale gaps. Radiative heat flux represents the propagated thermal information, while geometry and material dependent radiative coupling provides physically constrained weighting, and the temperature dependent optical response of phase-change materials enables nonlinear modulation and logic state control. Based on these primitives, we formulate a radiative thermal convolutional network for spatial information processing and a radiative thermal recurrent network for history dependent computation. The recurrent response is associated with radiative feedback, thermal relaxation, and phase change hysteresis, with VO2 providing history dependent short term memory and GST offering a possible route toward non volatile phase storage. We further distinguish the physical radiative networks from a separate software based inverse identification study, in which recurrent machine-learning models are trained on simulated near field heat flux temperature characteristics to recover structural parameters. By establishing a bottom up connection between fluctuational electrodynamics, radiative thermal logic, programmable thermal states, and neural network inspired computation, this study provides a physically grounded basis for exploring non contact thermal information processing at the near field limit. Comments: 26 pages, 5 figures, 3 tables Subjects: Mesoscale and Nanoscale Physics (cond-mat.mes-hall) ; Disordered Systems and Neural Networks (cond-mat.dis-nn) Cite as: arXiv:2608.06203 [cond-mat.mes-hall] (or arXiv:2608.06203v2 [cond-mat.mes-hall] for this version) https://doi.org/10.48550/arXiv.2608.06203 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Hexiang Zhang [ view email ] [v1] Thu, 6 Aug 2026 15:58:33 UTC (1,524 KB) [v2] Wed, 26 Aug 2026 18:01:09 UTC (1,451 KB) Full-text links: Access Paper: View PDF view license Current browse context: cond-mat.mes-hall < prev | next > new | recent | 2026-08 Change to browse by: cond-mat cond-mat.dis-nn References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

Related documents

• Beyond Electrons: A Theoretical Framework for Near-Field Radiative Thermal Computing and Neural-Network-Inspired Processing#677717
• Physics-informed Convolutional Neural Networks for Temperature Field Prediction of Heat Source Layout without Labeled Data#303005
• Efficient spectral Galerkin framework for nonlinear transient heat transfer in finite domains#1036951
• Composing Flow-Matching Energies with Known Physics: Generation, OOD Detection, and Inversion on PDE Fields#463213
• Automated ICRF heating surrogate modeling via machine learning#1145808