Irrotational Warp Lab

Research code for exploring irrotational (curl-free) shift-vector warp metrics and energy-condition diagnostics.

Quickstart

Installation

cd /home/echo_/Code/asciimath/irrotational-warp-lab

python -m venv .venv
. .venv/bin/activate
python -m pip install -e ".[dev]"

# Optional: Install GPU acceleration (requires CUDA 12.x)
pip install cupy-cuda12x

# Verify GPU availability (optional)
python scripts/check_gpu.py

Quick Examples

Basic 2D slice visualization:

python -m irrotational_warp plot-slice --rho 10 --sigma 3 --v 1.5 --n 101 \
  --out results/slice.png --json-out results/summary.json

Reproduce Rodal (2025) exact potential (CPU):

# Axisymmetric (2.5D) - fast
python scripts/reproduce_rodal_exact.py --mode axisym --nx 2400 --ny 1200 \
  --rho 5 --sigma 4 --v 1 --out results/rodal_axisym.json

# Full 3D - slower but complete
python scripts/reproduce_rodal_exact.py --mode 3d --n 100 \
  --rho 5 --sigma 4 --v 1 --out results/rodal_3d.json

GPU-accelerated 3D (requires CuPy):

python scripts/reproduce_rodal_exact.py --mode 3d --backend cupy --dtype float32 --n 120 \
  --rho 5 --sigma 4 --v 1 --out results/rodal_3d_gpu.json

Superluminal velocity sweep:

python scripts/sweep_superluminal.py --mode axisym --nx 1200 --ny 600 \
  --v-min 1.0 --v-max 3.0 --v-steps 20 --rho 5 --sigma 4 \
  --out results/superluminal_sweep.json

python scripts/plot_superluminal.py results/superluminal_sweep.json \
  --out results/superluminal_plot.png

Parameter optimization:

# Grid search + Nelder-Mead refinement (default)
python -m irrotational_warp optimize --method hybrid --refine \
  --sigma-min 2 --sigma-max 8 --v-min 0.8 --v-max 2.0 \
  --sigma-steps 10 --v-steps 10 --n 71 \
  --out results/optimization.json

# Bayesian optimization with Gaussian Process (efficient)
python -m irrotational_warp optimize --method bayes \
  --sigma-min 2 --sigma-max 8 --v-min 0.8 --v-max 2.0 \
  --n-calls 50 --n-initial 10 --random-state 42 --n 71 \
  --out results/optimization_bayes.json

# Compare methods
python scripts/test_bayesian_optimization.py

Run tests:

pytest -q

Contributing: See CONTRIBUTING.md for development guidelines.

See docs/TASKS.md for the complete research plan and milestones.

Reproducing Paper Results

All figures and results in the paper are fully reproducible. See results/README.md for the complete results registry.

Paper Build System

# Regenerate all figures and compile paper
make all

# Individual targets
make figures              # Regenerate all figures
make paper                # Compile LaTeX document
make test                 # Run test suite
make clean                # Remove build artifacts

Key Paper Results

Figure 1: 3D Convergence Study

python scripts/convergence_study_3d.py \
  --n-values 40,60,80,100 \
  --out results/experiments/convergence/study_3d.json

python scripts/make_paper_figures.py --figure convergence_3d

Figure 2: Superluminal Velocity Sweep

python scripts/sweep_superluminal.py --mode axisym --nx 1200 --ny 600 \
  --v-min 1.0 --v-max 3.0 --v-steps 20 --rho 5 --sigma 4 \
  --out results/experiments/superluminal/sweep_v1_to_3.json

python scripts/make_paper_figures.py --figure superluminal

Figure 3: Optimization Comparison

python scripts/test_bayesian_optimization.py  # Generates comparison data
python scripts/make_paper_figures.py --figure optimization

Computational Requirements

Typical runtimes (Intel i7, RTX 2060 Super):

• 2D slice (n=101): ~0.1s
• 3D volume (n=60, CPU): ~2s
• 3D volume (n=100, GPU): ~0.5s
• Bayesian optimization (50 calls, n=71): ~30s
• Full test suite (39 tests): ~5s

---

Latest Results

Post-M6 Implementation (Jan 17, 2026)

Completed milestones:

• ✅ GPU acceleration (CuPy backend, 5-10× speedup)
• ✅ 3D convergence validation (tail → 0.034%, approaching Rodal's ~0.04%)
• ✅ Superluminal studies (v ≤ 3, E ∝ v² confirmed)
• ✅ Bayesian optimization (5× fewer evaluations vs grid+NM)
• ✅ Physics invariant tests (11 regression tests)
• ✅ Paper infrastructure (LaTeX skeleton + figure pipeline)

Test Coverage: 39 tests (100% pass rate)

See docs/session-progress-2026-01-17-continued.md for details.

Paper Status

Draft manuscript: papers/irrotational_warp_metric.tex

To compile: make paper → Output: papers/irrotational_warp_metric.pdf