Updated 6/14/2021
Source code for simulating biohybrid swimming performance and using machine learning to iteratively select improved fin geometries
Fluid dynamics simulations are based in part off of work done by Oliver Hennigh, which can be found at the following: Github Link
| Filename | Description |
|---|---|
TF_LBM environment.yml |
File detailing the working conda environment |
CMAES_env.yml |
File detailing the working conda environment for CMAES |
| Filename | Description |
|---|---|
Swim Search.ipynb |
Main ML-DO Search algorithm for simulating rays |
SwimTravelMovie.ipynb |
After Sim - turning velocity profiles into movies |
PostDatabaseSearch.ipynb |
Evaluating ML model, and database results evaluation |
| Filename | Description |
|---|---|
NN Sampling Evaluation.ipynb |
Evaluating different search functions on a control dataset |
CMA-ES Compare.ipynb |
Evaluating CMAES on a control dataset |
Directed_Evolution_Compare.ipynb |
Evaluating DE on a control dataset |
All simulations and machine learning were performed Dell Precision 3630 Tower, equipped with an Intel Core i7-8700k CPU (3.7 GHz), 16 GB RAM, and an NVIDIA GeForce GTX 1080. Software was written in Python (V3.6.5), with TensorFlow (V2.0.0) used as backend for GPU acceleration.
TF_LBM environment.yml
Conda file for setting up the internal developing environment (IDE). Includes which packages are used for the rest of the files. Unless otherwise specified, this is the default enviroment used throughout for the rest of the code
CMAES_env.yml
Conda file for setting up the internal developing environment (IDE). Includes which packages are used for the rest of the files. Specifically used for evaluating CMAES in comparison to other search strategies (see CMA-ES Compare.ipynb)
Swim Search.ipynb
Main Jupyter notebook for integrating the total set of tasks in using ML-DO to search for efficient swimming in biohybrid fin geometries. This include generating a master list to store simulations results for each sDNA configurations. Then generating the initial search of which shapes to examine. Finally calls the main search algorithm, which creates a kinematic mesh of each fin shape, generates the vertex normal, solves their velocities for each frame, and then inserts this into the LBM fluid dynamics simulation. LBM model than simulates the relative travel speed of each swimmer, which is used as training data for the neural network model. Model then makes iterative suggestions on which swimmers to simulate next.
| Filename | Description |
|---|---|
DBG_LBM.py |
Code for performing a LBM fluid dynamics simulation |
GeoSwimmer.py |
Code for generating fin shapes from sDNA sequences |
is_point.py |
Code for checking if a lattice point is contained in a prism |
SwimMesh.py |
Code for solving fin kinematics and discretizing shapes |
SwimNN.py |
Code for defining the neural network model and displaying results |
Contraction_profile2.txt |
Relative contractile radius data per solid frame |
- Initial Database Generation: 5-15 minutes
- Kinematic Simulation: ~2-5 minutes to solve material interactions/ ray
- Fluid Dynamics Simulation: ~10-25 minutes per ray for 25 second simulation
- Machine Learning Training Time: ~15 minutes per iteration
results_updated.pkl
Main data file. Pickle database file containing the measured distance travelled by each
swimmer, as well as it’s updated model label as predicted by the neural network
CurrentSwimlist.txt
The list of which swimmer sDNA sequences will be simulated next in the current generation.
Contained as an external file outside of ram in case of memory leak/ simulation crash in
the middle of generations.
currentgen.txt
Counter file with an integer value listing the current generation number for swimmers.
Contained as an external file outside of ram in case of memory leak/ simulation crash
in the middle of generations.
{sDNA}_UV_kinematics.txt
File listing the sum of the horizontal (U) and vertical (V) velocity profiles generated by
kinematic contraction for a given sDNA sequence (wildcard denoted by *)
{sDNA}.h5
File containing the 3D fluid velocities fields for the final stroke of simulation (1 second
at 1 hz stimulation). Stored as a [1,x,y,z,vel] array with [1,x,y,z,0] = U, [1,x,y,z,1] = V,
[1,x,y,z,2] = W. can be used to reconstruct the final velocity fields.
CVVideo//{sDNA}_flow.avi
Video containing a top down projection of the fluid dynamics simulation for a single fin
(default recorded at 15 fps)
SwimVelocities//{sDNA}_velocity.txt
Text file containing a recording of the instantaneous velocity of a biohybrid fin geometry
(given for each fluid frame)
Total Run Time for 20 generations (1200 simulations): ~4 weeks
SwimTravelMovie.ipynb
Jupyter notebook file. Generates a video of a given fin geometry swimming. Requires that you have previously run an individual LBM simulation of that swimmer to generate the relative velocity profile.
| Filename | Description |
|---|---|
GeoSwimmer.py |
Code for generating fin shapes from sDNA sequences |
SwimMesh.py |
Code for solving fin kinematics and discretizing shapes |
Contraction_profile2.txt |
data on the relative contractile radius as a function of time |
Series of .png files graphing the swimming distance travelled by a geometry
Generating a single video: 2-5 minutes
PostDatabaseSearch.ipynb
Jupyter notebook file. Can be used to validate the final machine learning model, graph the resulting functional landscape, and find 'Top', 'Middle' and 'Bottom' ranked sDNA seqeuences. Assumes you have already run the swimsearch algorithim.
| Filename | Description |
|---|---|
GeoSwimmer.py |
Code for generating fin shapes from sDNA sequences |
SwimNN.py |
Code for defining the neural network model and displaying results |
Functional landscape graph, and a mean square error estiamte
Generating a single video: ~15 minutes to train final model
NN Sampling Evaluation.ipynb
Control study for examing different neural network architectures, sampling methods, and normalization approaches. Uses a a priori defined notional data set to evaluate relative performance. Data set is multimodal, and has synergestic combinations (defined by GenFooDataFrame function).
| Filename | Description |
|---|---|
GeoSwimmer.py |
Code for generating fin shapes from sDNA sequences |
provides a text output file which details the number of correctly identified top performing sequences. Additionally, provides graphs of the resulting functional space as mapped by the neural network model
~15 minutes to generate results database (can be reused once generated first time) ~2 minutes to train a network each generation ~2 hours to evaluate a given search method
CMA-ES Compare.ipynb
Control study for examing covariance matrix adapted evolutionary strategies as a search algorithim compared to ML-DO. Uses a a priori defined notional data set to evaluate relative performance. Data set is multimodal, and has synergestic combinations (defined by GenFooDataFrame function).
| Filename | Description |
|---|---|
GeoSwimmer.py |
Code for generating fin shapes from sDNA sequences |
Provides a text output file which details the number of correctly identified top performing sequences. Additionally, provides graphs of the sampled points projected into a 2D functional space representation.
~15 minutes to generate results database (can be reused once generated first time) ~4 hours to run the entire search
Directed_Evolution_Compare.ipynb
Control study for examing directed evolution as a search algorithim compared to ML-DO. Uses a a priori defined notional data set to evaluaterelative performance. Data set is multimodal, and has synergestic combinations (defined by GenFooDataFrame function).
| Filename | Description |
|---|---|
GeoSwimmer.py |
Code for generating fin shapes from sDNA sequences |
Provides a text output file which details the number of correctly identified top performing sequences. Additionally, provides graphs of the sampled points projected into a 2D functional space representation.
~15 minutes to generate results database (can be reused once generated first time) ~5 minutes to evaluate the entire search method
Copyright 2024 - Kevin Kit Parker
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