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Learning to reverse thermal diffusion
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DOI：10.1093/nsr/nwag287.png)
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Abstract
En 中文
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.
Keywords:
Thermal diffusion
Time-Reversal Operator
Physics-informed framework
Thermal retrodiction
Spatiotemporal analysis
Journal
IF:
17.1
Papers:
3.6K
Citations:
2.0W