Deep Fluids: A Generative Network for Parameterized Fluid Simulations

Introduction

This is a pytorch implementation of Deep Fluids paper. The original tensorflow implementation is located at https://github.com/byungsook/deep-fluids. The torch sources are located at src/. The sources of the original tensorflow implementation are kept where they were. This is the video playlist https://www.youtube.com/watch?v=nryZ-cqfs5E&list=PLX7INEUkOHp8TY3_uuC9EPbDhSbKJNfFt of the results obtained with the library. The OpenVDB files used for visualization in Blender can be obtained at https://drive.google.com/drive/folders/1eLe2T9eAdJaO5ckRroSzgaJDZFDcR_dB?usp=sharing The paper PyTorch implementation of Deep Fluids.pdf describes the experiments in detail.

Install prerequisites

First, create a conda environment for tensorflow, and install the original tensorflow paper requirements by following instructions from README_tensorflow.md. Manta package has to be compiled with the following call:

cmake .. -DGUI=OFF -DOPENMP=ON -DNUMPY=ON -DOPENVDB=ON

Install pytorch and tensorflow in separate conda environments to avoid dependency problems. Then, create a conda environment for pytorch, and install pytorch with GPU support and pytorch lightning package.

Install package

Run in Terminal:

pip install -e ./

to install the package.

Scenes

This project uses 3d smoke scenes from the original repo. There are two scenes we work in this implementation.

3d smoke scene with moving source

In this scene, scene/smoke3_mov.py, a smoke source is moving around randomly. In this experiments, we are going to:

• train an Autoencoder model
• run inference with the trained Autoencoder's Encoder model to obtain latent code representation
• train a time integrating Neural Network
• run inference with the trained Neural Network on simulated data from the Neural Network's validation dataset
• run inference with the trained Autoencoder's Generator model on the data produced by the Neural Network to obtain velocity fields

Generate training dataset

To generate a randomly moving source, call in Terminal with the tensorflow conda environment:

../manta/build/manta scene/smoke3_mov.py

this script populates the data/ directory with 200 simulations of 400 frames each. The velocity fields are placed into the data/smoke3_mov200_f400/v/ directory.

Train an Autoencoder model

To train a model, run the following script from a pytorch conda environment:

python -m deepfluids.train.autoencoder

my learning curves: 

Analyze the learning curves

Observe the training process from tensorboard by calling:

tensorboard --logdir experiments

and opening localhost/6006 in web browser:

Encode velocity fields into the latent code representation

To encode, run:

python -m deepfluids.test.autoencoder.encode 

Train a neural network

to train a Neural Network:

python -m deepfluids.train.nn

my learning curves: 

Run inference with the neural network

to run inference, call

python -m deepfluids.test.nn

Generate the velocity fields

Now, let's use the data generated by the neural network to produce the velocity fields:

python -m deepfluids.test.autoencoder.generate --nn_inference

Visualize the obtained velocity fields

Then, visualize the generated velocity field located at experiments/Autoencoder/velocity_field by calling

../manta/build/manta scene/smoke3_vel_advect.py 

this command calls manta advect method and writes the smoke density in the openVDB format to the data/smoke3_mov200_f400/d_vdb directory.

Import the generated VDB files into Blender->Add Volume

Use blender to visualize the velocity fields. To add Volume in Blender, click Add->Volume->Import openVDB.

3d smoke scene with varying velocity and buoyancy

In the scene, scene/smoke3_vel_buo.py, is used to train a generator. In this experiment, we condense the velocity field into a compact latent code representation. The latent code contains 3 elements: velocity, buoyancy and time. At inference, the Generator produces a velocity field using the latent code.

Generate training dataset

We vary the the velocity and buoyancy parameters for training data. To generate the training data, call from tensorflow conda environment:

../manta/build/manta scene/smoke3_vel_buo.py

Tweak the model structure to match you generated scene parameters

You might want to update the neural network structure depending on the parameters you generated the data with. See commented out lines in the model/generator.py module. This is an hacky at the moment and needs to be put into the Generator module initialization parameters.

Train a model

To train a model, call the following script from a pytorch conda environment:

python -m deepfluids.train.generator


Visualize

To visualize, call the following command in Terminal:

../manta/build/manta scene/smoke3_vel_buo_advect.py