Reversing the arrow of time in heat transfer: A novel operator learning framework for thermal retrodiction
Peer-Reviewed Publication
Updates every hour. Last Updated: 30-Oct-2026 06:16 ET (30-Oct-2026 10:16 GMT/UTC)
Thermal diffusion—a fundamental physical process pervasive in applications ranging from electronics to industrial systems—is inherently irreversible under the second law of thermodynamics. As temperature gradients spontaneously smooth out over time, crucial information regarding the initial thermal state is permanently lost due to entropy increase. This fundamental information loss leads to a severe ill-posedness in inverse thermal problems, where infinitesimal errors in the final observation can cause enormous uncertainties in the reconstructed initial state.
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