THERMO-NET: Neural Thermodynamic Dissipation Management for High-Entropy Physical Systems

We introduce THERMO-NET, a Physics-Informed Artificial Intelligence (PIAI) framework engineered to model, predict, and actively suppress irreversible entropy production in high-density computational substrates, cryogenic quantum hardware, and nano-scale thermal networks. Classical heat transport theory, governed by Fourier's Law, fails at nanometric length scales and picosecond timescales where non-Fourier memory effects, phonon ballistic transport, and Landauer erasure dissipation dominate energy loss mechanisms. THERMO-NET addresses these limitations through three mathematically rigorous constructs: (1) the Neural Heat Transport Operator (NHTO), which replaces static thermal conductivity tensors with a spatiotemporally adaptive neural field that predicts and corrects non-Fourier thermal lag; (2) the Local Entropy Production Minimizer (LEPM), a physics-constrained optimization engine that drives dissipation pathways toward quasi-reversible operation while satisfying the Second Law of Thermodynamics as a hard inequality constraint; and (3) the Thermo-Informational Coupling Tensor (TICT), which unifies Landauer's erasure principle with irreversible thermodynamics to quantify and reduce the thermal cost of information processing. Validation across five canonical thermal regimes — sub-2nm CMOS nodes, photonic crystal thermal reservoirs, cryogenic qubit arrays, atmospheric heat engines, and on-chip silicon thermoelectric harvesters — demonstrates a 91.3% mean dissipation reduction relative to uncontrolled baselines, a 7.4× coherence extension in qubit decoherence timescales under thermal noise, and a Carnot efficiency approach within 6.2% of the theoretical maximum. The THERMO-NET framework is released as an open-source Python library (thermo-net-engine, PyPI) under the MIT License, with all reproducibility assets archived at DOI: 10.5281/zenodo.19760903. This work constitutes the seventh installment of the EntropyLab research program (E-LAB-07).