Learning to reverse thermal diffusion

Clicks: 35
ID: 313461
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Star

Ranked #24 of 421 articles by views in national science review

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 421 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Abstract Thermal diffusion, which governs heat transfer across a wide range of systems-from electronics to industrial processes-is inherently irreversible under the second law of thermodynamics, thus obscuring time-dependent information. To overcome this ill-posedness, this study introduces a physics-informed framework that centers on a novel Time-Reversal Operator learning approach. A finite-difference-based network is first employed to robustly derive heterogeneous material properties. Crucially, the core innovation lies in the operator for thermal retrodiction. Distinct from traditional point-wise solvers, this functional model learns the mapping between function spaces, enabling the direct projection of the final-state thermal field back to its initial state. By synergizing analytical eigenbasis decomposition with frequency-domain operator learning, the Time-Reversal Operator effectively reconstructs the backward propagation of temperature fields. Validated on 3D-printed structures and chips, this operator-driven method achieves retrodiction errors below 0.1%, establishing a high-fidelity paradigm for spatiotemporal analysis. This breakthrough has broad implications for non-destructive testing in energy systems, with potential applications extending to a wide class of phenomena such as mass, charge, and light diffusion.
Reference Key
openalex_W7161233730 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Hanqi Chen, Qiang-Kai-Lai Huang, Yanxiang Wang, Yifan Shou, Pei‐Chao Cao, Wenduo Yu, Dong Wang, Zhun Wei, Hongsheng Chen, J Huang, Ying Li
Are you one of these authors? Claim your profile
Journal national science review
Year 2026
DOI
10.1093/nsr/nwag287
URL
https://doi.org/10.1093/nsr/nwag287
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.