E-LAB-07 | EntropyLab Research Program
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"Heat is not the enemy — unmanaged entropy is. THERMO-NET: Mastering the Dissipation." — THERMO-NET v1.0.0 Manifesto
- Overview
- The Problem
- Core Constructs
- Mathematical Architecture
- Validation Results
- Project Structure
- Installation
- Quick Start
- EntropyLab Program
- Reproducibility Infrastructure
- Author
- License
THERMO-NET is 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 replaces static thermal physics with three adaptive neural constructs that enforce thermodynamic laws as hard constraints rather than soft regularization targets.
Key achievements (v1.0.0):
| Metric | Result |
|---|---|
| Mean Thermal Efficiency Index (η_T) | 91.3% |
| Mean Entropy Production Reduction | 87.9% vs. uncontrolled baseline |
| Qubit Coherence Extension (T1 / T2) | 7.4× / 7.5× |
| Carnot Efficiency Approach | 93.8% of theoretical maximum |
| Validation Regimes | 5 canonical thermal environments |
| Prior Art Benchmark (classical MPC) | 74.6% η_T → THERMO-NET +16.7 pp |
Every instance of energy waste in a physical system — whether a CPU hotspot, a decoherent qubit, or an inefficient turbine — is fundamentally irreversible entropy production. Three domains suffer most acutely:
1. Nano-Scale Computing Below 2 nm transistor nodes, the phonon mean free path exceeds device dimensions. Heat no longer flows diffusively — it propagates ballistically, forming localized thermal hotspots on picosecond timescales that classical thermal models cannot predict or prevent.
2. Quantum Hardware Superconducting qubits at 15 mK lose coherence (T1, T2) due to thermal quasiparticle generation. Even sub-microkelvin fluctuations collapse quantum superposition states, imposing hard limits on fault-tolerant quantum error correction without active thermal management.
3. Energy Conversion Real heat engines operate 20–40% below the Carnot efficiency ceiling. The gap represents recoverable energy dissipated through viscous losses, finite-gradient conduction, and irreversible thermochemical reactions — all forms of unnecessary entropy production.
THERMO-NET addresses all three through a unified entropy minimization framework.
Generalizes the Cattaneo–Vernotte hyperbolic heat equation by replacing static thermal conductivity and scalar relaxation time with spatiotemporally adaptive neural fields.
- Architecture: SIREN (sinusoidal activation networks) — 6 hidden layers, width 512
- Captures: Phonon ballistic transport, spectral non-equilibrium, Kapitza interface resistance, Kerr-analog nonlinear thermal effects
- Constraint: Hard energy conservation loss — prevents hallucinated energy sources
Constructs the full entropy production rate field σ(r, t) and applies model-predictive control to drive it toward zero while enforcing the Second Law of Thermodynamics as a hard inequality constraint (σ ≥ 0 everywhere, always).
- Architecture: LSTM-256 predictor with 200 μs look-ahead horizon
- Optimizes: Jointly over heat flux, matter diffusion, viscous dissipation, and Landauer erasure channels
- Constraint: σ ≥ 0 (Second Law), T(r) ≤ T_max (material safety), |du/dt| ≤ u_rate (actuation limits)
Bridges Landauer's erasure principle with irreversible thermodynamics. Provides a spatially resolved tensor field Φ(r, t) that quantifies and minimizes the thermodynamic cost of information processing at each location in the computational domain.
- Structure: Hermitian positive-semidefinite matrix
- Diagonal elements: Direct Landauer erasure cost per computational state variable
- Off-diagonal elements: Cross-channel thermal coupling between erasure operations
- Limit: Perfect Landauer operation → all off-diagonal = 0, diagonal = k_B·T·ln(2) per bit
τ_θ(r,t) · ∂q/∂t + q(r,t) = −κ(r,t,θ)·∇T(r,t) + F_AI(r, T, ∇T, θ)
τ_θ: neural relaxation time field | κ(r,t,θ): adaptive conductivity tensor | F_AI: non-Fourier correction field
L_NHTO(θ) = λ₁·L_pde + λ₂·L_bc + λ₃·L_energy + λ₄·L_ballistic
λᵢ: adaptive NTK-balanced loss weights | L_energy: energy conservation | L_ballistic: phonon MFP regularization
σ(r,t) = q·∇(1/T) + Σᵢ Jᵢ·∇(−μᵢ/T) + Π_visc/T + σ_Landauer(r,t)
q: heat flux | Jᵢ: species flux | μᵢ: chemical potential | σ_Landauer: Landauer erasure dissipation
min_{u(t)} ∫₀ᵀ ∫_V σ(r,t,u) dr dt
subject to: σ(r,t) ≥ 0 ∀r,t
T(r) ≤ T_max
|du/dt| ≤ u_rate
Φᵢⱼ(r,t) = k_B · T(r,t) · (∂S_info/∂φᵢ)(r,t) · (∂σ_Landauer/∂φⱼ)(r,t) + Σ_ext_ij(r,t)
S_info: information-theoretic entropy | φᵢ: computational state variable | Σ_ext: external noise coupling
Validated across five canonical thermal regimes spanning 15 mK → 1200 K, 10⁻⁹ → 10¹² W/m³ power density, and Knudsen numbers from 10⁻⁴ (diffusive bulk) to 10⁴ (extreme ballistic).
| ID | Platform | Temperature | Primary Noise | η_T | σ Reduction |
|---|---|---|---|---|---|
| R1 | Sub-2nm CMOS Node | ~300 K | Ballistic phonon transport | 92.1% | 88.4% |
| R2 | Photonic Crystal Thermal Reservoir | ~100–400 K | Phonon–photon scattering | 91.7% | 86.9% |
| R3 | Cryogenic Superconducting Qubit Array | ~15 mK | Quasiparticle thermal noise | 93.4% | 91.3% |
| R4 | High-Efficiency Atmospheric Heat Engine | ~400–900 K | Viscous & conductive irreversibility | 89.6% | 85.2% |
| R5 | On-Chip Silicon Thermoelectric Harvester | ~250–600 K | Joule heating & Seebeck mismatch | 90.8% | 87.7% |
Regime R3 highlight: T1 extended from 47.3 μs → 351.4 μs (7.4×), T2 from 31.8 μs → 236.0 μs (7.5×). Values approach surface-code fault-tolerance threshold requirements.
Regime R4 highlight: Carnot efficiency gap closed from 31.7% → 6.2% below theoretical maximum.
Cross-regime generalization: Model pre-trained on R1–R3 achieved < 5.1 pp η_T degradation on unseen R4–R5 — no retraining required.
THERMO-NET/
│
├── README.md # This file
├── LICENSE # MIT License © 2026 Samir Baladi
├── CITATION.cff # Citation metadata
├── pyproject.toml # Build configuration
├── setup.py # Package setup
│
├── paper/
│ ├── THERMO-NET_Research_Paper.docx # Full academic paper (v1.0.0)
│ ├── THERMO-NET_Research_Paper.pdf # PDF version
│ └── figures/ # All paper figures and diagrams
│ ├── fig1_nhto_architecture.png
│ ├── fig2_lepm_control_loop.png
│ ├── fig3_tict_tensor_map.png
│ ├── fig4_validation_r1_r5.png
│ └── fig5_qubit_coherence_extension.png
│
├── thermo_net/ # Core Python library (thermo-net-engine)
│ ├── __init__.py
│ ├── version.py # v1.0.0
│ │
│ ├── physics/ # Physics Layer
│ │ ├── __init__.py
│ │ ├── cattaneo_vernotte.py # CV hyperbolic heat equation solver
│ │ ├── lindblad_thermal.py # Lindblad master equation (thermal channels)
│ │ ├── onsager_solver.py # Onsager reciprocal relation solver
│ │ ├── landauer_cost.py # Landauer erasure cost calculator
│ │ ├── entropy_production.py # σ(r,t) decomposition utilities
│ │ └── material_library.py # Thermal properties: Si, GaN, diamond, etc.
│ │
│ ├── neural/ # Neural Layer
│ │ ├── __init__.py
│ │ ├── nhto.py # Neural Heat Transport Operator (SIREN-6L)
│ │ ├── lepm.py # Local Entropy Production Minimizer (LSTM-256)
│ │ ├── tict.py # Thermo-Informational Coupling Tensor
│ │ ├── siren.py # SIREN architecture base class
│ │ └── loss_functions.py # Physics-informed loss: L_pde, L_energy, L_ballistic
│ │
│ ├── coupling/ # Coupling Layer
│ │ ├── __init__.py
│ │ ├── tict_compute.py # TICT tensor computation engine
│ │ ├── cross_channel_mapper.py # Multi-channel dissipation mapper
│ │ └── info_entropy_bridge.py # Information ↔ physical entropy bridge
│ │
│ ├── control/ # Control Layer
│ │ ├── __init__.py
│ │ ├── mpc_solver.py # Model Predictive Control (200 μs horizon)
│ │ ├── phase_locking.py # Phase-locking algorithm (inherited from PHOTON-Q)
│ │ └── actuation_interface.py # Electro-thermal actuator interface
│ │
│ └── interface/ # Interface Layer
│ ├── __init__.py
│ ├── thermal_state_tracker.py # ThermalStateTracker class (main API)
│ ├── regime_config.py # Regime configuration: R1–R5 + custom
│ ├── sensor_interface.py # Environmental sensor data ingestion
│ └── metrics_export.py # η_T, σ maps, coherence time export
│
├── benchmarks/ # Validation & benchmarking scripts
│ ├── run_all_regimes.py # Full 5-regime validation pipeline
│ ├── regime_r1_cmos.py # Sub-2nm CMOS benchmark
│ ├── regime_r2_photonic.py # Photonic crystal thermal reservoir
│ ├── regime_r3_qubit.py # Cryogenic qubit coherence benchmark
│ ├── regime_r4_heat_engine.py # Atmospheric heat engine Carnot approach
│ ├── regime_r5_thermoelectric.py # Silicon thermoelectric harvester
│ └── compare_baseline.py # THERMO-NET vs. classical MPC comparison
│
├── experiments/ # Raw experimental data & model weights
│ ├── data/
│ │ ├── r1_cmos_thermoreflectance/ # Time-resolved thermoreflectance measurements
│ │ ├── r2_photonic_crystal/ # Photonic crystal thermal field data
│ │ ├── r3_qubit_array/ # Qubit T1/T2 time series under thermal noise
│ │ ├── r4_heat_engine/ # Engine efficiency & entropy production logs
│ │ └── r5_thermoelectric/ # Seebeck coefficient & thermal gradient data
│ │
│ └── weights/
│ ├── nhto_pretrained_v1.0.0.pt # NHTO pre-trained weights (all 12 materials)
│ ├── lepm_lstm_v1.0.0.pt # LEPM LSTM weights (22 measurement stations)
│ └── tict_base_v1.0.0.pt # TICT base weights
│
├── training/ # Training pipeline
│ ├── train_nhto.py # 3-phase curriculum training (6,000 epochs)
│ ├── train_lepm.py # LSTM entropy predictor training
│ ├── train_tict.py # TICT fine-tuning per regime
│ ├── curriculum_phase1.py # Phase 1: 12-material Fourier baseline
│ ├── curriculum_phase2.py # Phase 2: Non-Fourier perturbation injection
│ ├── curriculum_phase3.py # Phase 3: Multi-regime experimental data
│ └── configs/
│ ├── nhto_config.yaml # NHTO hyperparameters
│ ├── lepm_config.yaml # LEPM/LSTM hyperparameters
│ └── training_defaults.yaml # AdamW, batch size, NTK rebalancing schedule
│
├── notebooks/ # Jupyter notebooks for exploration
│ ├── 01_nhto_walkthrough.ipynb # Neural Heat Transport Operator demo
│ ├── 02_lepm_entropy_maps.ipynb # Local entropy production visualization
│ ├── 03_tict_landauer_analysis.ipynb# TICT Landauer cost breakdown
│ ├── 04_qubit_coherence_demo.ipynb # R3 qubit coherence extension demo
│ └── 05_carnot_approach_demo.ipynb # R4 Carnot efficiency gap closure demo
│
├── docs/ # Documentation
│ ├── index.md # Documentation home
│ ├── api_reference.md # Full API reference
│ ├── math_appendix.md # Extended mathematical derivations
│ ├── regime_guide.md # How to configure custom thermal regimes
│ └── entropylab_context.md # THERMO-NET within the EntropyLab program
│
└── .gitlab-ci.yml # CI/CD pipeline (lint, test, benchmark)
Requirements: Python ≥ 3.10, PyTorch ≥ 2.3, NumPy ≥ 2.0, SciPy ≥ 1.13
# From PyPI (stable)
pip install thermo-net-engine
# From source (development)
git clone https://gitlab.com/gitdeeper11/THERMO-NET.git
cd THERMO-NET
pip install -e .from thermo_net import ThermalStateTracker
import numpy as np
# Initialize tracker for a 128-node silicon substrate
tracker = ThermalStateTracker(
spatial_dim=128,
lstm_hidden=256,
material='Si',
regime='R1_cmos'
)
# Load pre-trained weights
tracker.load_weights('experiments/weights/nhto_pretrained_v1.0.0.pt')
# Run one control step (1 ps timestep, 300K substrate)
T_field = np.ones((128, 128)) * 300.0 # K
power_map = np.random.rand(128, 128) * 1e10 # W/m³ — sub-2nm switching transient
ops_rate = 1e18 # operations/sec
state = tracker.step(
dt=1e-12,
env_obs={
'T_field': T_field,
'power_map': power_map,
'op_rate': ops_rate
}
)
print(f"η_T = {state.efficiency:.4f}") # Thermal efficiency index
print(f"σ = {state.entropy_production:.4e}") # W/(m³·K) — entropy production rate
print(f"QOEI = {state.tict_diagonal.mean():.4f}") # Mean Landauer cost per operationQubit regime (R3):
tracker_cryo = ThermalStateTracker(
spatial_dim=64,
lstm_hidden=256,
material='Al', # Aluminum — superconducting transmon substrate
regime='R3_qubit',
T_operating=0.015 # 15 mK
)
state = tracker_cryo.step(
dt=1e-9,
env_obs={
'T_field': np.ones((64, 64)) * 0.015,
'power_map': np.random.rand(64, 64) * 1e-9,
'op_rate': 1e6 # gate operations/sec
}
)
print(f"T1_extended = {state.coherence_T1 * 1e6:.1f} μs")
print(f"T2_extended = {state.coherence_T2 * 1e6:.1f} μs")THERMO-NET is E-LAB-07 within the nine-project EntropyLab research program, which builds a unified Physics-Informed Artificial Intelligence architecture for entropy-governed physical systems.
| ID | Project | Domain | Status |
|---|---|---|---|
| E-LAB-01 | ENTROPIA | Unified Dissipation State Function (Boltzmann + Shannon) | ✅ Published |
| E-LAB-02 | ENTRO-AI | LLM Thermodynamic Phase Transitions & Entropy-Driven Throttling | ✅ Published |
| E-LAB-03 | PHOTON-Q | Neural Wavefront Intelligence for Quantum-Optical Systems | ✅ Published |
| E-LAB-04 | ENTRO-ENGINE | Multi-System Entropy Budget Coordination Law | ✅ Published |
| E-LAB-05 | ENTRO-EVO | Adaptive Entropy Weighting Optimizer for Cross-Domain Transfer | ✅ Published |
| E-LAB-06 | ION-Logic | Neural Ionic Transport for Electrochemical Systems | ✅ Published |
| E-LAB-07 | THERMO-NET | Neural Thermodynamic Dissipation Management | ✅ This project |
| E-LAB-08 | (In development) | — | 🔄 Active |
| E-LAB-09 | (In development) | — | 🔄 Active |
DOI chain:
- ENTROPIA (E-LAB-01):
10.5281/zenodo.19416737 - ENTRO-AI (E-LAB-02):
10.5281/zenodo.19284086 - ENTRO-EVO (E-LAB-05):
10.5281/zenodo.19464489 - THERMO-NET (E-LAB-07):
10.5281/zenodo.19760903
All experimental data, pre-trained model weights, training scripts, validation benchmarks, and reproduction scripts are fully archived and publicly accessible.
| Platform | Identifier / URL | Content |
|---|---|---|
| GitLab (Primary) | gitlab.com/gitdeeper11/THERMO-NET |
Source code, CI/CD, Issues |
| GitHub (Mirror) | github.com/gitdeeper11/THERMO-NET |
Mirror repository |
| Codeberg (Mirror) | codeberg.org/gitdeeper11/THERMO-NET |
Mirror repository |
| Bitbucket (Mirror) | bitbucket.org/gitdeeper11/THERMO-NET |
Mirror repository |
| Zenodo | 10.5281/zenodo.19760903 |
Archived release, DOI, Datasets |
| PyPI | pip install thermo-net-engine |
Python library (v1.0.0) |
| Netlify | https://thermo-net.netlify.app |
Interactive demo + docs |
| ORCID | 0009-0003-8903-0029 |
Author identifier |
| OSF | EntropyLab parent project | Preregistrations + data |
All outputs reported in the research paper are fully reproducible by running:
python benchmarks/run_all_regimes.py --weights experiments/weights/ --data experiments/data/@software{baladi2026thermonet,
author = {Baladi, Samir},
title = {THERMO-NET: Neural Thermodynamic Dissipation Management
for High-Entropy Physical Systems},
version = {1.0.0},
year = {2026},
month = {April},
publisher = {Zenodo},
doi = {10.5281/zenodo.19760903},
url = {https://doi.org/10.5281/zenodo.19760903},
note = {E-LAB-07, EntropyLab Research Program},
orcid = {0009-0003-8903-0029}
}Samir Baladi Ronin Institute / Rite of Renaissance Independent Researcher — EntropyLab Program
- 📧 gitdeeper@gmail.com
- 🔗 ORCID: 0009-0003-8903-0029
- 📞 +1 (614) 264-2074
- 🌐 entropia-lab.netlify.app
MIT License
Copyright © 2026 Samir Baladi
Permission is hereby granted, free of charge, to any person obtaining a copy
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The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
THERMO-NET v1.0.0 — E-LAB-07 — EntropyLab Research Program © 2026 Samir Baladi — Ronin Institute / Rite of Renaissance — MIT License DOI: 10.5281/zenodo.19760903