peterzs / diff_stokes_flow Goto Github PK

Codebase for Functional Optimization of Fluidic Devices with Differentiable 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 <PARDISO_HOME> 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 <PARDISO_HOME>=/home/pardiso.

• Set PARDISO_LIC_PATH and OMP_NUM_THREADS:

export OMP_NUM_THREADS=4
export PARDISO_LIC_PATH=<PARDISO_HOME>
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.
• superposition_gate: run the Superposition Gate demo (Fig. 4) in the paper.

Run python refinement.py to generate Fig. 8 in the paper.

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: 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.

Rendering

Run python pbrt_renderer_demo.py to see how to use the Python wrapper of pbrt-v3.