tide-GPR 0.0.30

Torch-based Inversion & Development Engine for electromagnetic wave propagation

TIDE

Torch-based Inversion & Development Engine

TIDE is a PyTorch-based library for high-frequency electromagnetic wave propagation and inversion, built on Maxwell's equations. It provides CPU and CUDA implementations for forward modeling, gradient computation, and full-waveform inversion workflows.

Features

• Maxwell Equation Solvers:
  • 2D TM mode propagation
  • 3D Maxwell propagation
• Automatic Differentiation: Gradient support through PyTorch's autograd hooks
• High Performance: Optimized C/CUDA kernels for critical operations
• Flexible Storage: Device/CPU/disk snapshot modes for gradient computation
• Staggered Grid: Standard FDTD staggered grid implementation
• PML Boundaries: Perfectly Matched Layer absorbing boundaries
• Snapshot Compression: Optional BF16 snapshot compression on the default path

Feature Matrix

Capability	Entry Point	Status	Notes
2D TM forward modeling	tide.maxwelltm	Stable	Primary onboarding path
2D TM inversion / autograd	tide.maxwelltm, MaxwellTM	Stable	Uses PyTorch autograd
3D forward modeling	tide.maxwell3d	Stable	Supports component selection
3D inversion / gradients	tide.maxwell3d, Maxwell3D	Stable with constraints	Check the limitations guide before scaling up
Snapshot storage modes	storage_mode=*	Stable	Device, CPU, disk, none, and auto
Callbacks	forward_callback, backward_callback	Stable	Keep callback work lightweight
Debye dispersion	DebyeDispersion	Advanced	Requires explicit time-step validation

Installation

From PyPI

Ensure you have proper PyTorch installation with CUDA binding for your system.

For CUDA environments, you may need to install a CUDA-enabled PyTorch build first:

uv pip install torch --index-url https://download.pytorch.org/whl/cu128

The cu128 tag is for CUDA 12.8. Replace it based on your CUDA version.

Then install TIDE via uv or pip:

uv pip install tide-GPR

or

pip install tide-GPR

From Source

We recommend using uv for building:

git clone https://github.com/vcholerae1/tide.git
cd tide
uv build

To rebuild only the native backend during development:

bash scripts/build_csrc.sh

Requirements:

• Python >= 3.12
• PyTorch >= 2.12
• CUDA Toolkit (optional, for GPU support)
• CMake >= 3.28 (optional, for building from source)

Quick Start

import torch
import tide

# Create a simple model
nx, ny = 200, 100
epsilon = torch.ones(ny, nx) * 4.0  # Relative permittivity
sigma = torch.zeros_like(epsilon)    # Conductivity (S/m)
mu = torch.ones_like(epsilon)        # Relative permeability
epsilon[50:, :] = 9.0  # Add a layer

# Set up source
source_amplitude = tide.ricker(
    freq=4e8,           # 400 MHz
    length=1000,
    dt=1e-11,
    peak_time=5e-10
).reshape(1, 1, -1)

source_location = torch.tensor([[[10, 100]]], dtype=torch.long)
receiver_location = torch.tensor([[[10, 150]]], dtype=torch.long)

# Run forward simulation
*_, receiver_data = tide.maxwelltm(
    epsilon=epsilon,
  sigma=sigma,
  mu=mu,
  grid_spacing=0.01,
    dt=1e-11,
  source_amplitude=source_amplitude,
  source_location=source_location,
  receiver_location=receiver_location,
    pml_width=10
)

print(f"Recorded data shape: {receiver_data.shape}")

Core Modules

• tide.maxwelltm: 2D TM solver
• tide.maxwell3d: 3D solver
• tide.wavelets: Source wavelet generation
• tide.callbacks: Callback state and factories
• tide.storage: Snapshot storage and compression controls
• tide.workflow: Shot batching, receiver loss, and optimizer workflow helpers
• tide.resampling: CFL resampling helpers
• tide.cfl: CFL condition helper
• tide.padding: Padding and interior masking helpers
• tide.validation: Input validation helpers

Precision and Storage

Storage and precision controls:

out = tide.maxwelltm(
    epsilon,
    sigma,
    mu,
    grid_spacing=0.02,
    dt=4e-11,
    source_amplitude=src,
    source_location=src_loc,
    receiver_location=rec_loc,
    storage_mode="auto",
    storage_compression="bf16",
)

Notes:

• storage_mode accepts device, cpu, disk, none, and auto.
• storage_compression accepts none or bf16 for float32 snapshot storage on Maxwell and Born native paths that save backward intermediates.

Documentation

Recommended reading path:

1. docs/getting-started.md for installation and the first 2D forward run
2. docs/guides/api-orientation.md for choosing between tide.maxwelltm, tide.maxwell3d, MaxwellTM, and Maxwell3D
3. docs/guides/modeling.md and docs/guides/inversion.md for forward modeling and inversion workflows
4. docs/guides/configuration.md for storage, callbacks, backend, and CFL-related controls
5. docs/guides/limitations.md and docs/guides/verification.md before enabling advanced features broadly

Testing

Run the test suite:

pytest tests/

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Acknowledgments

This project includes code derived from Deepwave by Alan Richardson. We gratefully acknowledge the foundational work that made TIDE possible.

Citation

If you use TIDE in your research, please cite:

@software{tide2025,
  author = {Vcholerae1},
  title = {TIDE: Torch-based Inversion \& Development Engine},
  year = {2025},
  url = {https://github.com/vcholerae1/tide}
}

License

This project is licensed under the MIT License - see the LICENSE file for details.