GRAVI-NEURAL: Covariant Neural Characterization of Metric Tensor Perturbations in Dynamic Gravitational Environments
Authors/Creators
-
1.
Ronin Institute for Independent Scholarship 2.0
- 2. Rite of Renaissance
Description
GRAVI-NEURAL is a physics-informed artificial intelligence framework that introduces a Covariant Neural Operator (CNO) for learning, approximating, and evolving solutions to the Einstein Field Equations (EFE) under dynamic and strong-field gravitational conditions.
The system decomposes the spacetime metric into a Minkowski background and a learned neural perturbation field, enabling real-time prediction of curvature dynamics, gravitational waveforms, and geodesic trajectories.
GRAVI-NEURAL enforces physical consistency through Hamiltonian constraints and Bianchi identity preservation, ensuring energy-momentum conservation across all predictions. The architecture integrates multiple neural components to achieve coordinate-independent spacetime modeling and high-precision gravitational inference.
This project is part of the EntropyLab research program and aims to replace computationally intensive numerical relativity solvers with efficient, scalable, and physically consistent neural approximations applicable to gravitational wave astronomy, autonomous space navigation, and planetary geophysics.
DOI: 10.5281/zenodo.19871822
Version: v1.0.0
Year: 2026
License: MIT License
Files
GRAVI-NEURAL_E-LAB-08_v1.0.0.pdf
Files
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Additional details
Related works
- Is published in
- Software: https://pypi.org/project/gravi-neural-engine/1.0.0/ (URL)
- Is source of
- Dataset: https://gitlab.com/gitdeeper11/GRAVI-NEURAL (URL)
- Is supplement to
- Journal article: 10.5281/zenodo.19871822 (DOI)
Software
- Repository URL
- https://github.com/gitdeeper11/GRAVI-NEURAL
- Programming language
- Python
- Development Status
- Active
References
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- Bekenstein, J. D. (1973). Black holes and entropy. Physical Review D, 7(8), 2333–2346.
- Einstein, A. (1915). Die Feldgleichungen der Gravitation. Sitzungsberichte der Königlich Preußischen Akademie der Wissenschaften, 844–847.
- Hawking, S. W. (1975). Particle creation by black holes. Communications in Mathematical Physics, 43(3), 199–220.
- Jacobson, T. (1995). Thermodynamics of Spacetime: The Einstein Equation of State. Physical Review Letters, 75(7), 1260–1263.
- Li, Z., et al. (2021). Fourier Neural Operator for Partial Differential Equations. International Conference on Learning Representations (ICLR).
- Lindblom, L., Owen, B. J., & Brown, D. A. (2008). Model waveform accuracy standards for gravitational wave data analysis. Physical Review D, 78, 124020.
- Lu, L., Jin, P., Pang, G., Zhang, Z., & Karniadakis, G. E. (2021). Learning nonlinear operators via DeepONet. Nature Machine Intelligence, 3, 218–229.
- Pretorius, F. (2005). Evolution of Binary Black-Hole Spacetimes. Physical Review Letters, 95, 121101.
- Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2019). Physics-informed neural networks: A deep learning framework for solving forward and inverse problems. Journal of Computational Physics, 378, 686–707.