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 UMLENR
pip install -r requirements.txtThis 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.