Repository: UMLENR GitHub Repository
This repository explores the application of machine learning algorithms to better understand and optimize Low Energy Nuclear Reactions (LENR) and Lattice-Assisted Nuclear Reactions (LANR). By leveraging data analytics and machine learning, we aim to shed light on the complex mechanisms behind LENR, accelerating its development as a clean and abundant energy source.
LENR has long been a subject of scientific curiosity and debate. Despite its promise for clean and abundant energy, the underlying mechanisms remain poorly understood. This project aims to use machine learning to analyze existing LENR data and predict outcomes of various experimental setups.
- Data Preprocessing: Scripts for preprocessing LENR datasets.
- Machine Learning Models: Predict LENR outcomes using regression, classification, and clustering techniques.
- Simulation Framework: Tools and algorithms to simulate LENR events, including fusion cross-sections, reaction rates, and excess heat generation.
- Photo-Electric Effects Simulation: Models electron densities and momentum in the photoelectric effect from the Planck scale up to the molecular scale.
- Electron Interaction Simulation: Generates a cubic array of electrons and calculates the Coulomb interaction energy between them.
- Decay Process Simulation: Models the decay processes of various isotopes, including tritium and short-lived hydrogen isotopes.
- Visualization: Interactive visualization tools for data analysis and simulation results.
git clone https://github.com/ConsciousEnergy/UMLENR.git
cd UMLENRNote: Individual simulations have their own dependencies. See specific simulation sections below for requirements. For the full mathematical simulation framework, see the LENR Mathematical Simulation Framework section.
This simulation models 2D Lattice Confinement Fusion. The source code can be found here.
Simulates interactions and calculates total energy in a cubic array of electrons. The source code can be found here.
Models electron densities and momentum in the photoelectric effect in hydrogen. The source code can be found here.
A suite of simulations for various LENR phenomena. The source code can be found here.
Simulates MagnetoHydroDynamics using the Lattice Boltzmann method. The source code can be found here.
- LENRAutoGPT.py: Source Code
- LENRAutoGPT_v0_2.py: Source Code
- LENRAutoGPT_v0_3.py: Source Code
- LENR_ML_Recursive_Research_paper_Draft.txt: Source Code
- LENR_Theories: Directory
- LENR_Theories.csv: Source Code
- LENR_theoretical_model.py: Source Code
- LENRsimCrew.py: Source Code
- R&DWrite_crew.py: Source Code
- Simulation_Equations.txt: Source Code
- lenr_simulation.py: Source Code
- llama-2-70b-chat-agent(raw-code).ipynb: Source Code
We welcome contributions! Please see the CONTRIBUTING.md file for details on how to get involved.
This project is licensed under the GNU-3.0 License - see the LICENSE file for details.
- Special thanks to LENR-LANR.org for its extensive open access library to LENR.
- Shoutout to the machine learning community for providing invaluable resources and tools:
By harnessing the capabilities of machine learning and fostering collaborative efforts, UMLENR aims to make significant advancements in understanding and harnessing LENR and LANR, paving the way for groundbreaking developments in clean energy technology.
A comprehensive mathematical simulation framework for Low-Energy Nuclear Reactions (LENR) based on the unified theoretical framework presented in "Theoretical and Mathematical Framework for Low-Energy Nuclear Reactions (LENR)" by Diadon Acs and LENR-ARA (February 2025).
This project implements advanced quantum mechanical simulations, including:
- Quantum tunneling calculations with electron screening
- Coupled Poisson-Schrödinger solvers
- Monte Carlo uncertainty propagation
- Machine learning parameter discovery
- Real-time 3D visualizations
- Comprehensive statistical analysis
- Quantum Tunneling: Gamow factors, WKB approximation, coherent multi-body effects
- Electron Screening: Modified Coulomb barriers in metallic lattices
- Interface Dynamics: Electric double layers, field enhancement
- Lattice Effects: Phonon coupling, coherent domains
- Bubble Dynamics: Rayleigh-Plesset collapse, shock-induced localization
- Numerical Solvers: Coupled PDE solvers with adaptive mesh refinement
- Monte Carlo: Statistical sampling with error propagation
- Sensitivity Analysis: Sobol indices, parametric thresholds
- Machine Learning: Parameter optimization, pattern recognition
- 3D Rendering: Interactive lattice structures, field visualization
- Real-time Updates: WebSocket streaming of simulation progress
- Statistical Analysis: Confidence bounds, significance testing
- Data Export: Multiple formats for further analysis
- Python 3.11+
- Node.js 18+
- PostgreSQL 14+ (optional, for result storage)
- Redis (optional, for caching)
- Clone the repository and navigate to the framework:
git clone https://github.com/ConsciousEnergy/UMLENR.git
cd UMLENR/LENR_Math_SimImportant: The LENR Mathematical Simulation Framework is a complete application located in the LENR_Math_Sim/ directory. All subsequent commands must be run from within this directory (UMLENR/LENR_Math_Sim/).
- Set up Python environment:
# Create virtual environment (from LENR_Math_Sim directory)
python -m venv venv
# Activate (Windows)
venv\Scripts\activate
# Activate (Linux/Mac)
source venv/bin/activate
# Install backend dependencies (path is relative to LENR_Math_Sim directory)
pip install -r backend/requirements.txtNote: The backend/requirements.txt file contains all Python dependencies for the FastAPI backend, including scientific computing libraries (NumPy, SciPy), machine learning frameworks (PyTorch, TensorFlow), and visualization tools (PyVista, VTK).
- Set up Node environment:
cd frontend
npm install- Configure environment variables:
# Copy example configuration
cp .env.example .env
# Edit .env with your settings- Start the backend server:
cd backend
uvicorn main:app --reload --host 0.0.0.0 --port 8000- Start the frontend (in a new terminal):
cd frontend
npm start- Access the application:
- Frontend: http://localhost:3000
- API Documentation: http://localhost:8000/docs
- WebSocket: ws://localhost:8000/ws
Using Docker Compose:
docker-compose up --buildfrom backend.core.quantum_tunneling import QuantumTunneling, TunnelingParameters
# Configure parameters
params = TunnelingParameters(
temperature=300.0, # K
electric_field=1e9, # V/m
screening_energy=25.0, # eV
loading_ratio=0.95
)
# Create calculator
tunneling = QuantumTunneling(params)
# Calculate enhancement
energy = 10.0 # eV
results = tunneling.calculate_total_enhancement(energy)
print(f"Total enhancement: {results['total_enhancement']:.2e}")
# Run Monte Carlo simulation
sim_results = tunneling.simulate_tunneling_events(n_samples=10000)# Start a simulation
curl -X POST http://localhost:8000/api/v1/simulations \
-H "Content-Type: application/json" \
-d '{
"material": "Pd-D",
"temperature": 300,
"loading_ratio": 0.95,
"monte_carlo_samples": 10000
}'
# Get results
curl http://localhost:8000/api/v1/results/{simulation_id}const ws = new WebSocket('ws://localhost:8000/ws');
ws.onmessage = (event) => {
const data = JSON.parse(event.data);
console.log('Simulation update:', data);
};
ws.send(JSON.stringify({
action: 'subscribe',
simulation_id: 'abc123'
}));LENR_Math_Sim/
├── backend/ # Python FastAPI backend
│ ├── core/ # Physics engine
│ ├── solvers/ # Numerical methods
│ ├── ml/ # Machine learning
│ └── api/ # REST endpoints
├── frontend/ # React UI
│ ├── components/ # UI components
│ ├── services/ # API integration
│ └── store/ # State management
├── notebooks/ # Jupyter notebooks
├── docs/ # Documentation
└── tests/ # Test suites
- Gamow penetration factor
- WKB approximation
- Coherent multi-body tunneling (Takahashi model)
- Temperature-averaged reaction rates
- Thomas-Fermi screening
- Debye screening
- Interface electron dynamics
- Modified Rydberg matter effects
- Phonon-nuclear coupling (Hagelstein model)
- Coherent domains (Preparata QED)
- Defect-assisted energy localization
- Poisson-Schrödinger coupling
- Interface electric fields
- Casimir effects in nanocavities
- Bubble collapse dynamics
The simulation results have been validated against:
- Historical LENR experiments (Fleischmann-Pons, McKubre)
- Modern calorimetric data (Mizuno, Levi)
- Isotopic measurements (Arata-Zhang, Miley)
- Statistical significance tests (p < 0.05)
We welcome contributions! Please see CONTRIBUTING.md for guidelines.
- Fork the repository
- Create a feature branch
- Implement changes with tests
- Submit pull request with description
- Pass code review and CI/CD checks
Run the test suite:
# Backend tests
cd backend
pytest --cov=. --cov-report=html
# Frontend tests
cd frontend
npm test
# Integration tests
docker-compose -f docker-compose.test.yml up- API Documentation - Interactive API docs (when server is running)
- LENR_Math_Sim README - Framework-specific documentation
- Development Plan - Development roadmap
- API Documentation - API reference
- Setup Instructions - Detailed setup guide
- Project Structure - Architecture overview
- Tunneling calculation: ~0.1 ms per energy point
- Monte Carlo (10k samples): ~2 seconds
- Poisson-Schrödinger solver: ~5 seconds for 100x100 grid
- 3D visualization: 60 FPS for 1000 particles
- NumPy vectorization for array operations
- Numba JIT compilation for hot loops
- Parallel processing with multiprocessing/Dask
- GPU acceleration available via CuPy/JAX
This project is licensed under the MIT License - see LICENSE file.
If you use this software in your research, please cite:
@software{lenr_math_sim_2025,
title={LENR Mathematical Simulation Framework},
author={Diadon Acs and LENR-ARA},
year={2025},
url={https://github.com/ConsciousEnergy/UMLENR}
}- Project Lead: Diadon Acs
- Repository: https://github.com/ConsciousEnergy/UMLENR
- Issues: https://github.com/ConsciousEnergy/UMLENR/issues
This work builds upon decades of LENR research and theoretical developments from:
- Fleischmann & Pons (electrochemical fusion)
- Takahashi (tetrahedral symmetric condensates)
- Hagelstein (phonon-nuclear coupling)
- Preparata (QED coherent domains)
- Widom-Larsen (ultra-low momentum neutrons)
Special thanks to the LENR research community for continued dedication to advancing this field.
Note: This is an active research project. Results should be interpreted within the context of ongoing scientific investigation into LENR phenomena.