# Functional Optimization of Fluidic Devices with Differentiable Stokes Flow (Siggraph Asia 2020) [![Travis CI Status](https://travis-ci.org/mit-gfx/diff_stokes_flow.svg?branch=master)](https://travis-ci.org/mit-gfx/diff_stokes_flow) ## System requirement - Ubuntu 18.04 - (Mini)conda 4.7.12 or higher ## Installation ``` git clone --recursive https://github.com/mit-gfx/diff_stokes_flow.git cd diff_stokes_flow conda env create -f environment.yml conda activate diff_stokes_flow ./install.sh ``` ### (Optional) Configuring Pardiso Let `` be the folder that you saved your Pardiso license file and the binary file. For example, if `/home/pardiso/pardiso.lic` and `/home/pardiso/libpardiso600-GNU720-X86-64.so` are your license and binary files, then `=/home/pardiso`. - Set `PARDISO_LIC_PATH` and `OMP_NUM_THREADS`: ``` export OMP_NUM_THREADS=4 export PARDISO_LIC_PATH= export PARDISOLICMESSAGE=1 ``` - Pardiso requires `lapack` and `blas`: ``` sudo apt-get install liblapack-dev sudo apt-get install libblas-dev ``` As of the date this README is written, the version we use is `3.7.1-4ubuntu1`: ``` Reading package lists... Done Building dependency tree Reading state information... Done libblas-dev is already the newest version (3.7.1-4ubuntu1). liblapack-dev is already the newest version (3.7.1-4ubuntu1). 0 upgraded, 0 newly installed, 0 to remove and 132 not upgraded. ``` - Recompile the codebase with an optional `pardiso` argument: ``` ./install.sh pardiso ``` ## Examples Navigate to the `python/example` folder before you run any of the following scripts. ### Results in the paper Run `python run_demo.py [demo_name]` where `[demo_name]` can be any of the following; - `amplifier`: run the `Amplifier` demo (Fig. 2) in the paper. - `flow_averager`: run the `Flow Averager` demo (Fig. 4) in the paper. flow_averager - `superposition_gate`: run the `Superposition Gate` demo (Fig. 4) in the paper. superposition_gate - `funnel`: run the `Funnel` demo (Fig. 4) in the paper. funnel - `fluidic_twister`: run the `Fluidic Twister` demo (Fig. 5) in the paper. Note that the original result was run with a resolution of 64 by 64 by 32, and we downsampled it in this codebase for a faster demonstration. fluidic_twister - `fluidic_switch`: run the `Fluidic Switch` demo (Fig. 1) in the paper. Note that the original result was run with a resolution of 64 by 64 by 32, and we downsampled it in this codebase for a faster demonstration. The two figures below show the optimized solution in two modes: fluidic_switch_mode_0 fluidic_switch_mode_1 ### Numerical tests Run `python [script_name].py` where `[script_name]` can be any of the following: - `bezier_2d`: show the level-set of a Bezier curve and check the implementation of gradients. - `cell_2d`: check if all quantities in `Cell2d` are implemented correctly. - `scene_2d`: check the gradients of loss defined in a 2-dimensional scene. - `shape_composition_2d` and `shape_composition_3d`: check the gradients of composing multiple primitive level-sets. Finally, if you would like to run all these numerical experiments above, you can simply call `run_all_tests`: - `run_all_tests`: this will sequentially run all the aforementioned numerical tests. Run `python refinement.py` to generate the numerical resutls in Fig. 8 in the paper. ### Rendering Run `python pbrt_renderer_demo.py` to see how to use the Python wrapper of pbrt-v3. Additionally, we have also provided a few Python scripts for generating figures in the presentation: - `python draw_pipeline.py`: generate each subfigure in the whole pipeline. - `python draw_design.py`: generate pictures for the design section. - `python draw_discretization.py`: generate pictures for the discretization section. - `python draw_optimization.py`: generate video and pictures for the optimization section. ## How to cite ```text @article{du2020stokes, author = {Du, Tao and Wu, Kui and Spielberg, Andrew and Matusik, Wojciech and Zhu, Bo and Sifakis, Eftychios}, title = {Functional Optimization of Fluidic Devices with Differentiable Stokes Flow}, year = {2020}, issue_date = {December 2020}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {39}, number = {6}, issn = {0730-0301}, url = {https://doi.org/10.1145/3414685.3417795}, doi = {10.1145/3414685.3417795}, journal = {ACM Trans. Graph.}, month = dec, articleno = {197}, numpages = {15}, keywords = {physically-based simulation, fluid simulation, computational design optimization} } ``` ## Contact If you have any questions about the paper or the codebase, please feel free to contact taodu@csail.mit.edu.