autograv 0.1.1

Automatic differentiation for numerical relativity using JAX

autograv

Bridging numerical relativity and automatic differentiation using JAX

autograv is a Python library that uses JAX and automatic differentiation to compute various tensors and quantities from Einstein's general theory of relativity. Given a metric function, it can calculate Christoffel symbols, curvature tensors, and solve the Einstein field equations with high numerical precision.

Features

• Automatic Differentiation: Uses JAX's jax.jacfwd for forward-mode automatic differentiation to compute derivatives of metric tensors with exact numerical precision
• Tensor Calculus: Leverages jax.numpy.einsum for efficient Einstein summation notation operations
• High Precision: Configured to use 64-bit floating point arithmetic for maximum accuracy
• Pure Functions: All computations are functional and composable

What can you compute?

Given a metric tensor function, autograv can compute:

• Christoffel symbols (affine connection coefficients)
• Torsion tensor (verification that connection is symmetric)
• Riemann curvature tensor (intrinsic curvature of spacetime)
• Ricci tensor and Ricci scalar (curvature related to volume change)
• Einstein tensor (left-hand side of Einstein field equations)
• Stress-energy-momentum tensor (mass-energy content)
• Kretschmann invariant (scalar curvature for detecting singularities)

Installation

# Using uv
uv pip install autograv

# Or using pip
pip install autograv

Quick Start

import jax.numpy as jnp
from autograv import (
    spherical_polar_metric,
    christoffel_symbols,
    riemann_tensor,
    einstein_tensor,
)

# Define coordinates
coordinates = jnp.array([5, jnp.pi/3, jnp.pi/2], dtype=jnp.float64)

# Compute Christoffel symbols for the 2-sphere
christoffels = christoffel_symbols(coordinates, spherical_polar_metric)
print(christoffels)

# Compute Riemann tensor
riemann = riemann_tensor(coordinates, spherical_polar_metric)
print(riemann)

Examples

The examples/ directory contains complete examples:

• sphere_example.py: Computing quantities for a 2-sphere metric
• schwarzschild_example.py: Computing quantities for the Schwarzschild black hole metric

Run them with:

uv run python examples/sphere_example.py
uv run python examples/schwarzschild_example.py

How it Works

Automatic Differentiation

Traditional approaches to computing derivatives in physics use either:

• Symbolic differentiation: Exact but computationally expensive
• Numerical differentiation: Fast but prone to floating-point errors

Automatic differentiation (autodiff) combines the best of both worlds by:

1. Tracing computational operations to build a directed acyclic graph (DAG)
2. Computing gradients via the chain rule by traversing the graph
3. Achieving exact numerical precision at machine precision limits

JAX Integration

JAX provides:

• jax.jacfwd: Forward-mode autodiff for computing Jacobians
• jax.numpy.einsum: Efficient Einstein summation for tensor operations
• NumPy-compatible API with GPU/TPU acceleration support

Example: Christoffel Symbols

Given a metric tensor g_ij, the Christoffel symbols are:

Γ^j_kl = (1/2) g^jm (∂g_mk/∂x^l + ∂g_lm/∂x^k - ∂g_kl/∂x^m)

In code:

def christoffel_symbols(coordinates, metric):
    g = metric(coordinates)
    g_inv = jnp.linalg.inv(g)
    jacobian = jax.jacfwd(metric)(coordinates)  # Automatic differentiation!
    
    return 0.5 * jnp.einsum('jm, klm -> jkl', g_inv,
                            jnp.einsum('klm -> mkl', jacobian) +
                            jnp.einsum('klm -> lmk', jacobian) - jacobian)

API Reference

Metrics

• minkowski_metric(coordinates): Flat spacetime metric
• spherical_polar_metric(coordinates): 2-sphere metric in (r, θ, φ)

Core Functions

• christoffel_symbols(coordinates, metric): Affine connection coefficients
• torsion_tensor(coordinates, metric): Antisymmetric part of connection
• riemann_tensor(coordinates, metric): Curvature tensor
• ricci_tensor(coordinates, metric): Trace of Riemann tensor
• ricci_scalar(coordinates, metric): Scalar curvature
• kretschmann_invariant(coordinates, metric): Curvature invariant
• einstein_tensor(coordinates, metric): G_ij = R_ij - (1/2)g_ij R
• stress_energy_momentum_tensor(coordinates, metric): T_ij from Einstein equations

Utilities

• close_to_zero(func): Decorator to suppress near-zero numerical noise
• TOLERANCE: Threshold for zero suppression (default: 1e-8)

Requirements

• Python 3.11+
• JAX (CPU-only on Windows, GPU/TPU support on Linux/macOS)
• NumPy

Future Work

• Add more standard metrics (Kerr, Kerr-Newman, FRW, etc.)
• Implement Weyl tensor and Weyl invariant
• Support for JIT compilation with @jax.jit
• GPU/TPU acceleration examples
• Integration with differential equation solvers
• Visualization tools for curvature

License

MIT

Acknowledgments

Based on concepts from the blog post "Bridging numerical relativity and automatic differentiation using JAX". This project demonstrates the synergy between modern machine learning tools and classical physics computations.

References

• JAX Documentation
• Mathematical Methods for Physics by Sadri Hassani
• Einstein's General Theory of Relativity