ThomasHelfer/TorchGRTL
A translation of crucial parts of GRTL in torch for accelerated learning
This project helps theoretical physicists and computational relativists apply deep learning techniques to problems in Numerical Relativity. It takes in existing numerical relativity data, such as simulations of binary black holes, and outputs trained models that can calculate complex quantities like Christoffel symbols, Hamiltonian, and Momentum constraints more efficiently. Researchers in gravitational wave astrophysics or cosmology who work with high-dimensional spacetime data would use this.
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Use this if you are a physicist working with numerical relativity data and want to leverage deep learning to accelerate calculations of gravitational fields and spacetime curvature.
Not ideal if you are looking for a general-purpose deep learning library or are not familiar with the concepts and data structures of numerical relativity.
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Language
Jupyter Notebook
License
MIT
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Last pushed
Nov 26, 2024
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