| Name | diffSPH JSON |
| Version |
0.1.3
JSON |
| download |
| home_page | None |
| Summary | A Fully differentiable SPH Solver. |
| upload_time | 2025-07-24 10:42:52 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | None |
| license | Apache License
Version 2.0, January 2004
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|
| keywords |
sph
radius
pytorch
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
numba
scipy
numpy
toml
tqdm
torch
torchvision
torchaudio
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
<!-- # diffSPH - A Framework for differentiable SPH Simulations -->
diffSPH is an open-source Smoothed Particle Hydrodynamics simulation framework built for adjoint problem solving and machine learning applications. diffSPH is written mostly in Python using PyTorch but also supports C++/CUDA extensions. The fundamentally differentiable nature of diffSPH make it a versatile basis for many different applications of SPH. Stay tunes for more details on the applications and design of diffSPH! This is the solver at [https://diffsph.fluids.dev/](https://diffsph.fluids.dev/) .
## Installation
The only major requirement for this solver is our companion software that handles the neighbor searching and related topics, which you can find more about [here](https://github.com/wi-re/torchCompactRadius). Other than that the solver is straightforward to setup and can also be used in Google colab
```
conda create --name sphEnv python=3.12
conda activate sphEnv
conda install -c anaconda ipykernel -y
conda install nvidia/label/cuda-12.8.1::cuda-toolkit cudnn
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128
git clone https://https://github.com/wi-re/torchCompactRadius
cd torchCompactRadius
python setup.py develop
cd ..
https://github.com/tum-pbs/diffSPH
cd diffSPH
pip install -e .
```
## Examples
### Compressible Flows
<table>
<tbody>
<tr>
<td style="width: 25%;"><a href="https://github.com/tum-pbs/diffSPH/blob/main/examples/compressible/rayleighTaylor2d.ipynb"><img src="examples/compressible/videos/rayleighTaylor.gif"></a></td>
<td style="width: 25%;"><a href="https://github.com/tum-pbs/diffSPH/blob/main/examples/compressible/KevinHelmHoltz.ipynb"><img src="examples/compressible/videos/kevin_helmholtz.png"></a></td>
<td style="width: 25%;"><a href="https://github.com/tum-pbs/diffSPH/blob/main/examples/compressible/sod_1d.ipynb"><img src="examples/compressible/sod_1D.png"></a></td>
<td style="width: 25%;"><a href="https://github.com/tum-pbs/diffSPH/blob/main/examples/compressible/sedov_2d.ipynb"><img src="examples/compressible/videos/sedov_2D.png"></a></td>
</tr>
<tr>
<td align="center">Rayleigh-Taylor Instability</td>
<td align="center">Kevin-Helmholtz Instability</td>
<td align="center">Sod-Shock Tube</td>
<td align="center">Sedov Blastwave</td>
</tr>
</tbody>
</table>
More examples can be found under examples/compressible
### Weakly Compressibe Flows
<table>
<tbody>
<tr>
<td style="width: 34%;"><a href="https://github.com/tum-pbs/diffSPH/blob/main/examples/weaklyCompressible/05_TGV.ipynb"><img src="examples/weaklyCompressible/scripts/videos/05_taylorGreenVortex_2.png"></a></td>
<td style="width: 33%;"><a href="https://github.com/tum-pbs/diffSPH/blob/main/examples/weaklyCompressible/03_RotatingSquarePatch.ipynb"><img src="examples/weaklyCompressible/scripts/videos/03_rotatingSquarePatch.png"></a></td>
<td style="width: 33%;"><a href="https://github.com/tum-pbs/diffSPH/blob/main/examples/weaklyCompressible/09_LDC.ipynb"><img src="examples/weaklyCompressible/scripts/videos/09_lidDrivenCavity.png"></a></td>
</tr>
<tr>
<td align="center">Taylor Green Vortex</td>
<td align="center">Rotating Square Patch</td>
<td align="center">Lid-Driven Cavity</td>
</tr>
<tr>
<td style="width: 33%;"><a href="https://github.com/tum-pbs/diffSPH/blob/main/examples/weaklyCompressible/13_DrivenSquare.ipynb"><img src="examples/weaklyCompressible/scripts/videos/13_DrivenSquare.png"></a></td>
<td style="width: 34%;"><a href="https://github.com/tum-pbs/diffSPH/blob/main/examples/weaklyCompressible/18_flowPastObstacle.ipynb"><img src="examples/weaklyCompressible/scripts/videos/18_flowPastSphere.png"></a></td>
<td style="width: 33%;"><a href="https://github.com/tum-pbs/diffSPH/blob/main/examples/weaklyCompressible/15_Dambreak.ipynb"><img src="examples/weaklyCompressible/scripts/videos/15_damBreak.png"></a></td>
</tr>
<tr>
<td align="center">Driven Square</td>
<td align="center">Flow Past Cylinder</td>
<td align="center">Dam Break</td>
</tr>
</tbody>
</table>
More examples can be found under examples/weaklyCompressible
### Incompressible Flows:
<table>
<tbody>
<tr>
<td style="width: 40%;"><img src="examples/videos/05_taylorGreenVortex_2.gif"></td>
<td style="width: 60%;"></td>
</tr>
<tr>
<td align="center">Taylor Green Vortex</td>
</tr>
</tbody>
</table>
### Other Examples
diffSPH can also be used to simulate a variety of other PDEs such as the wave equation
<table>
<tbody>
<tr>
<td style="width: 40%;"><img src="examples/waveEqn2.gif"></td>
<td style="width: 60%;"></td>
</tr>
<tr>
<td align="center">Wave Equation 2D</td>
</tr>
</tbody>
</table>
### Inverse Problems
diffSPH is meant as a framework for differentiable problem solving and supports all the common tasks in adjoint optimization and machine learning, including:
<table>
<tbody>
<tr>
<td style="width: 25%;"><a href="https://github.com/tum-pbs/diffSPH/blob/main/gradients/autoShift.ipynb"><img src="examples/noisyDistributionShift2D_gradient.png"></a></td>
<td style="width: 25%;"><img src="examples/gammaOptim.png"></td>
<td style="width: 25%;"><a href="https://github.com/tum-pbs/diffSPH/blob/main/waveEquation/waveEqn.ipynb"><img src="examples/waveOptimized.png"></a></td>
<td style="width: 25%;"><a href="https://github.com/tum-pbs/diffSPH/blob/main/waveEquation/waveEqnInTheLoop.ipynb"><img src="examples/waveEqn_wUnroll.png"></a></td>
</tr>
<tr>
<td align="center">Loss-based Physics</td>
<td align="center">Parameter Optimization</td>
<td align="center">Shape Optimization</td>
<td align="center">Solver In The Loop</td>
</tr>
</tbody>
</table>
## Features
diffSPH comes with a large variety of SPH schemes already builtin and can be easily extended to include more solver schemes and SPH properties:
- $\delta$-SPH and $\delta^+$-SPH for weakly compressible simulations
- IISPH and DFSPH for incompressible simulations
- CompSPH, CRKSPH, PESPH and the classic Monaghan scheme for compressible simulations
- mDBC boundary conditions for rigid bodies
- Inlets and Oulets with buffer zones
- Periodic BCs using minimum image conventions
- Neumann and Dirichlet BCs
- grad-H, kernel renormalization and CRK correction schemes
- $\delta^+$ and implicit particle shifting
- Monaghan and Owen schemes for adaptive particle support radii
- Balsara, Morris, Rosswog, Cullen Dehnen artificial viscosity switches
- Sub particle scale turbulence modelling
- Differentiable generation of initial conditions using SDFs
- Hierarchical and compact hashing based neighbor searching
- Verlet lists for neighborhood searches
- Most Common SPH Kernel Functions (Wendland, B-Spline, Poly6) used across the fields
pip install toml scipy numba tqdm h5py matplotlib ipywidgets ipympl imageio scikit-image
### Time Integration
diffSPH comes builtin with a large number of temporal integration schemes which we validated against a dampened harmonic oscillator with a hidden state variable:
$$
\begin{aligned}
\hat{k} &= k + \alpha e\\
\frac{dx}{dt} &= u&\\
m \frac{du}{dt} &= -\hat{k} x &- c u\\
\frac{de}{dt} &= \beta \text{sgn}(u) u^2 &- \gamma e
\end{aligned}
$$
<table>
<tbody>
<tr>
<td style="width: 25%;"><img src="examples/image-11.png"></td>
<td style="width: 25%;"><img src="examples/image-12.png"></td>
</tr>
<tr>
<td align="center">Physical Problem</td>
<td align="center">Integrator Accuracy</td>
</tr>
</tbody>
</table>
### Datasets
There have been a variety of datasets already generated with diffSPH, including:
<table>
<tbody>
<tr>
<td style="width: 50%;"><a href="https://huggingface.co/datasets/thuerey-group/SFBC_dataset_II"><img src="https://huggingface.co/datasets/thuerey-group/SFBC_dataset_II/resolve/main/data.png"></a></td>
<td style="width: 50%;"><a href="https://huggingface.co/datasets/Wi-Re/wcsph_flows"><img src="examples/periodicDS.png"></a></td>
</tr>
<tr>
<td align="center">SFBC Test Case II</td>
<td align="center">Periodic BCs</td>
</tr>
<tr>
<td style="width: 50%;"><a href="https://huggingface.co/datasets/Wi-Re/wcsph_flows_no_slip"><img src="examples/noSlip.png"></a></td>
<td style="width: 50%;"><a href="https://huggingface.co/datasets/Wi-Re/wcsph_flows_free_slip"><img src="examples/freeSlip.png"></a></td>
</tr>
<tr>
<td align="center">No-Slip</td>
<td align="center">Free-Slip</td>
</tr>
</tbody>
</table>
## Publications
So far diffSPH has been used in the following publications:
- Symmetric Fourier Basis Convolutions for Learning Lagrangian Fluid Simulations, R. Winchenbach, N. Thuerey, International Conference on Learning Representations 2024 https://arxiv.org/abs/2403.16680
Workshop Papers:
- MoriNet: A Machine Learning-based Mori-Zwanzig Perspective on Weakly Compressible SPH, R. Winchenbach and N. Thuerey, 2025 International SPHERIC Workshop Barcelona, Spain (pdf available under publications)
- Physically-Motivated Machine Learning Models for Lagrangian Fluid Mechanics, R. Winchenbach and N.Thuerey, 2024 International SPHERIC Workshop Berlin, Germany (presentation and more information available here https://fluids.dev/spheric2024/)
- Cross-Validation of SPH-based Machine Learning Models using the Taylor-Green Vortex Case - R. Winchenbach and N. Thuerey - Particle Methods and Applications Conference 2024 Santa Fe, USA (presentation and more information available here: https://pmac.fluids.dev)
- A Hybrid Framework for Fluid Flow Simulations: Combining SPH with Machine Learning - R. Winchenbach and N. Thuerey - 2023 International SPHERIC Workshop Rhodes, Greece (pdf available under publications)
## Acknoledgement
This work has been in parts funded by the DFG Individual Research Grant TH 2034/1-2.
Raw data
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"description": "\n<!-- # diffSPH - A Framework for differentiable SPH Simulations -->\n\ndiffSPH is an open-source Smoothed Particle Hydrodynamics simulation framework built for adjoint problem solving and machine learning applications. diffSPH is written mostly in Python using PyTorch but also supports C++/CUDA extensions. The fundamentally differentiable nature of diffSPH make it a versatile basis for many different applications of SPH. Stay tunes for more details on the applications and design of diffSPH! This is the solver at [https://diffsph.fluids.dev/](https://diffsph.fluids.dev/) .\n\n## Installation\n\nThe only major requirement for this solver is our companion software that handles the neighbor searching and related topics, which you can find more about [here](https://github.com/wi-re/torchCompactRadius). Other than that the solver is straightforward to setup and can also be used in Google colab\n\n```\nconda create --name sphEnv python=3.12\nconda activate sphEnv\nconda install -c anaconda ipykernel -y\nconda install nvidia/label/cuda-12.8.1::cuda-toolkit cudnn\npip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128\ngit clone https://https://github.com/wi-re/torchCompactRadius\ncd torchCompactRadius\npython setup.py develop\ncd ..\nhttps://github.com/tum-pbs/diffSPH\ncd diffSPH\npip install -e .\n```\n\n\n## Examples\n\n### Compressible Flows\n\n<table>\n <tbody>\n <tr>\n <td style=\"width: 25%;\"><a href=\"https://github.com/tum-pbs/diffSPH/blob/main/examples/compressible/rayleighTaylor2d.ipynb\"><img src=\"examples/compressible/videos/rayleighTaylor.gif\"></a></td>\n <td style=\"width: 25%;\"><a href=\"https://github.com/tum-pbs/diffSPH/blob/main/examples/compressible/KevinHelmHoltz.ipynb\"><img src=\"examples/compressible/videos/kevin_helmholtz.png\"></a></td>\n <td style=\"width: 25%;\"><a href=\"https://github.com/tum-pbs/diffSPH/blob/main/examples/compressible/sod_1d.ipynb\"><img src=\"examples/compressible/sod_1D.png\"></a></td>\n <td style=\"width: 25%;\"><a href=\"https://github.com/tum-pbs/diffSPH/blob/main/examples/compressible/sedov_2d.ipynb\"><img src=\"examples/compressible/videos/sedov_2D.png\"></a></td>\n </tr>\n <tr>\n <td align=\"center\">Rayleigh-Taylor Instability</td>\n <td align=\"center\">Kevin-Helmholtz Instability</td>\n <td align=\"center\">Sod-Shock Tube</td>\n <td align=\"center\">Sedov Blastwave</td>\n </tr>\n </tbody>\n</table>\n\nMore examples can be found under examples/compressible\n\n### Weakly Compressibe Flows\n\n<table>\n <tbody>\n <tr>\n <td style=\"width: 34%;\"><a href=\"https://github.com/tum-pbs/diffSPH/blob/main/examples/weaklyCompressible/05_TGV.ipynb\"><img src=\"examples/weaklyCompressible/scripts/videos/05_taylorGreenVortex_2.png\"></a></td>\n <td style=\"width: 33%;\"><a href=\"https://github.com/tum-pbs/diffSPH/blob/main/examples/weaklyCompressible/03_RotatingSquarePatch.ipynb\"><img src=\"examples/weaklyCompressible/scripts/videos/03_rotatingSquarePatch.png\"></a></td>\n <td style=\"width: 33%;\"><a href=\"https://github.com/tum-pbs/diffSPH/blob/main/examples/weaklyCompressible/09_LDC.ipynb\"><img src=\"examples/weaklyCompressible/scripts/videos/09_lidDrivenCavity.png\"></a></td>\n </tr>\n <tr>\n <td align=\"center\">Taylor Green Vortex</td>\n <td align=\"center\">Rotating Square Patch</td>\n <td align=\"center\">Lid-Driven Cavity</td>\n </tr>\n <tr>\n <td style=\"width: 33%;\"><a href=\"https://github.com/tum-pbs/diffSPH/blob/main/examples/weaklyCompressible/13_DrivenSquare.ipynb\"><img src=\"examples/weaklyCompressible/scripts/videos/13_DrivenSquare.png\"></a></td>\n <td style=\"width: 34%;\"><a href=\"https://github.com/tum-pbs/diffSPH/blob/main/examples/weaklyCompressible/18_flowPastObstacle.ipynb\"><img src=\"examples/weaklyCompressible/scripts/videos/18_flowPastSphere.png\"></a></td>\n <td style=\"width: 33%;\"><a href=\"https://github.com/tum-pbs/diffSPH/blob/main/examples/weaklyCompressible/15_Dambreak.ipynb\"><img src=\"examples/weaklyCompressible/scripts/videos/15_damBreak.png\"></a></td>\n </tr>\n <tr>\n <td align=\"center\">Driven Square</td>\n <td align=\"center\">Flow Past Cylinder</td>\n <td align=\"center\">Dam Break</td>\n </tr>\n </tbody>\n</table>\n\nMore examples can be found under examples/weaklyCompressible\n\n### Incompressible Flows:\n\n<table>\n <tbody>\n <tr>\n <td style=\"width: 40%;\"><img src=\"examples/videos/05_taylorGreenVortex_2.gif\"></td>\n <td style=\"width: 60%;\"></td>\n </tr>\n <tr>\n <td align=\"center\">Taylor Green Vortex</td>\n </tr>\n </tbody>\n</table>\n\n### Other Examples\n\ndiffSPH can also be used to simulate a variety of other PDEs such as the wave equation\n\n<table>\n <tbody>\n <tr>\n <td style=\"width: 40%;\"><img src=\"examples/waveEqn2.gif\"></td>\n <td style=\"width: 60%;\"></td>\n </tr>\n <tr>\n <td align=\"center\">Wave Equation 2D</td>\n </tr>\n </tbody>\n</table>\n\n### Inverse Problems\n\ndiffSPH is meant as a framework for differentiable problem solving and supports all the common tasks in adjoint optimization and machine learning, including:\n\n<table>\n <tbody>\n <tr>\n <td style=\"width: 25%;\"><a href=\"https://github.com/tum-pbs/diffSPH/blob/main/gradients/autoShift.ipynb\"><img src=\"examples/noisyDistributionShift2D_gradient.png\"></a></td>\n <td style=\"width: 25%;\"><img src=\"examples/gammaOptim.png\"></td>\n <td style=\"width: 25%;\"><a href=\"https://github.com/tum-pbs/diffSPH/blob/main/waveEquation/waveEqn.ipynb\"><img src=\"examples/waveOptimized.png\"></a></td>\n <td style=\"width: 25%;\"><a href=\"https://github.com/tum-pbs/diffSPH/blob/main/waveEquation/waveEqnInTheLoop.ipynb\"><img src=\"examples/waveEqn_wUnroll.png\"></a></td>\n </tr>\n <tr>\n <td align=\"center\">Loss-based Physics</td>\n <td align=\"center\">Parameter Optimization</td>\n <td align=\"center\">Shape Optimization</td>\n <td align=\"center\">Solver In The Loop</td>\n </tr>\n </tbody>\n</table>\n\n## Features\n\ndiffSPH comes with a large variety of SPH schemes already builtin and can be easily extended to include more solver schemes and SPH properties:\n\n- $\\delta$-SPH and $\\delta^+$-SPH for weakly compressible simulations\n- IISPH and DFSPH for incompressible simulations\n- CompSPH, CRKSPH, PESPH and the classic Monaghan scheme for compressible simulations\n- mDBC boundary conditions for rigid bodies\n- Inlets and Oulets with buffer zones\n- Periodic BCs using minimum image conventions\n- Neumann and Dirichlet BCs\n- grad-H, kernel renormalization and CRK correction schemes\n- $\\delta^+$ and implicit particle shifting\n- Monaghan and Owen schemes for adaptive particle support radii\n- Balsara, Morris, Rosswog, Cullen Dehnen artificial viscosity switches\n- Sub particle scale turbulence modelling\n- Differentiable generation of initial conditions using SDFs\n- Hierarchical and compact hashing based neighbor searching\n- Verlet lists for neighborhood searches\n- Most Common SPH Kernel Functions (Wendland, B-Spline, Poly6) used across the fields\n\npip install toml scipy numba tqdm h5py matplotlib ipywidgets ipympl imageio scikit-image\n\n### Time Integration\n\ndiffSPH comes builtin with a large number of temporal integration schemes which we validated against a dampened harmonic oscillator with a hidden state variable:\n\n$$\n\\begin{aligned}\n\\hat{k} &= k + \\alpha e\\\\\n\\frac{dx}{dt} &= u&\\\\\nm \\frac{du}{dt} &= -\\hat{k} x &- c u\\\\\n\\frac{de}{dt} &= \\beta \\text{sgn}(u) u^2 &- \\gamma e\n\\end{aligned}\n$$\n<table>\n <tbody>\n <tr>\n <td style=\"width: 25%;\"><img src=\"examples/image-11.png\"></td>\n <td style=\"width: 25%;\"><img src=\"examples/image-12.png\"></td>\n </tr>\n <tr>\n <td align=\"center\">Physical Problem</td>\n <td align=\"center\">Integrator Accuracy</td>\n </tr>\n </tbody>\n</table>\n\n\n\n### Datasets\n\nThere have been a variety of datasets already generated with diffSPH, including:\n\n\n<table>\n <tbody>\n <tr>\n <td style=\"width: 50%;\"><a href=\"https://huggingface.co/datasets/thuerey-group/SFBC_dataset_II\"><img src=\"https://huggingface.co/datasets/thuerey-group/SFBC_dataset_II/resolve/main/data.png\"></a></td>\n <td style=\"width: 50%;\"><a href=\"https://huggingface.co/datasets/Wi-Re/wcsph_flows\"><img src=\"examples/periodicDS.png\"></a></td>\n </tr>\n <tr>\n <td align=\"center\">SFBC Test Case II</td>\n <td align=\"center\">Periodic BCs</td>\n </tr>\n <tr>\n <td style=\"width: 50%;\"><a href=\"https://huggingface.co/datasets/Wi-Re/wcsph_flows_no_slip\"><img src=\"examples/noSlip.png\"></a></td>\n <td style=\"width: 50%;\"><a href=\"https://huggingface.co/datasets/Wi-Re/wcsph_flows_free_slip\"><img src=\"examples/freeSlip.png\"></a></td>\n </tr>\n <tr>\n <td align=\"center\">No-Slip</td>\n <td align=\"center\">Free-Slip</td>\n </tr>\n </tbody>\n</table>\n\n\n## Publications\n\nSo far diffSPH has been used in the following publications:\n\n- Symmetric Fourier Basis Convolutions for Learning Lagrangian Fluid Simulations, R. Winchenbach, N. Thuerey, International Conference on Learning Representations 2024 https://arxiv.org/abs/2403.16680\n\nWorkshop Papers:\n- MoriNet: A Machine Learning-based Mori-Zwanzig Perspective on Weakly Compressible SPH, R. Winchenbach and N. Thuerey, 2025 International SPHERIC Workshop Barcelona, Spain (pdf available under publications)\n- Physically-Motivated Machine Learning Models for Lagrangian Fluid Mechanics, R. Winchenbach and N.Thuerey, 2024 International SPHERIC Workshop Berlin, Germany (presentation and more information available here https://fluids.dev/spheric2024/)\n- Cross-Validation of SPH-based Machine Learning Models using the Taylor-Green Vortex Case - R. Winchenbach and N. Thuerey - Particle Methods and Applications Conference 2024 Santa Fe, USA (presentation and more information available here: https://pmac.fluids.dev)\n- A Hybrid Framework for Fluid Flow Simulations: Combining SPH with Machine Learning - R. Winchenbach and N. Thuerey - 2023 International SPHERIC Workshop Rhodes, Greece (pdf available under publications)\n\n## Acknoledgement\n\nThis work has been in parts funded by the DFG Individual Research Grant TH 2034/1-2.\n\n\n\n\n\n\n",
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