Authors:
- Nikolas Borrel ([email protected])
- Allan P. Engsig-Karup ([email protected])
- Cheol-Ho Jeong ([email protected])
Code for sound field predictions in domains with Neumann and impedance boundaries. Used for generating results from the paper "Physics-informed neural networks for 1D sound field predictions with parameterized sources and impedance boundaries" by N. Borrel-Jensen, A. P. Engsig-Karup, and C. Jeong.
Run
Train
Run
python3 main_train.py --path_settings="path/to/script.json"
Scripts for setting up models with Neumann, frequency-independent and dependent boundaries can be found in scripts/settings (see JSON settings).
Evaluate
Run
python3 main_evaluate.py
The settings are
id_dir = <unique id> settings_filename = 'settings.json' base_dir = "path/to/base/dir" do_plots_for_paper = <bool> do_animations = <bool> do_side_by_side_plot = <bool>
The id_dir corresponds to the output directory generated after training, settings_filename is the name of the settings file used for training (located inside the id_dir directory), base_dir is the path to the base directory (see Input/output directory structure).
Evaluate model execution time
To evaluate the execution time of the surrogate model, run
python3 main_evaluate_timings.py --path_settings="path/to/script.json" --trained_model_tag="trained-model-dir"
The trained_model_tag is the directory with the trained model weights trained using the scripts located at the path given in path_settings.
Settings
Input/output directory structure
The input data should be located in a specific relative directory structure as (data used for the paper can be downloaded here)
base_path/
trained_models/
trained_model_tag/
checkpoint
cp.ckpt.data-00000-of-00001
cp.ckpt.index
training_data/
freq_dep_1D_2000.00Hz_sigma0.2_c1_d0.02_srcs3.hdf5
...
freq_indep_1D_2000.00Hz_sigma0.2_c1_xi5.83_srcs3.hdf5
...
neumann_1D_2000.00Hz_sigma0.2_c1_srcs3.hdf5
...
The reference data are located inside the training_data/ directory generated, where the data for impedance boundaries are generated using our SEM simulator, and for Neumann boundaries, the Python script main_generate_analytical_data.py was used.
Output result data are located inside the results folder
base_path/
results/
id_folder/
figs/
models/
LossType.PINN/
checkpoint
cp.ckpt.data-00000-of-00001
cp.ckpt.index
settings.json
The settings.json file is identical to the settings file used for training indicated by the --path_settings argument. The directory LossType.PINN contains the trained model weights.
JSON settings
The script scripts/settings/neumann.json was used for training the Neumann model from the paper
{
"id": "neumann_srcs3_sine_3_256_7sources_loss02",
"base_dir": "../data/pinn",
"c": 1,
"c_phys": 343,
"___COMMENT_fmax___": "2000Hz*c/343 = 5.8309 for c=1, =23.3236 for c=4",
"fmax": 5.8309,
"tmax": 4,
"xmin": -1,
"xmax": 1,
"source_pos": [-0.3,-0.2,-0.1,0.0,0.1,0.2,0.3],
"sigma0": 0.2,
"rho": 1.2,
"ppw": 5,
"epochs": 25000,
"stop_loss_value": 0.0002,
"boundary_type": "NEUMANN",
"data_filename": "neumann_1D_2000.00Hz_sigma0.2_c1_srcs7.hdf5",
"batch_size": 512,
"learning_rate": 0.0001,
"optimizer": "adam",
"__comment0__": "NN setting for the PDE",
"activation": "sin",
"num_layers": 3,
"num_neurons": 256,
"ic_points_distr": 0.25,
"bc_points_distr": 0.45,
"loss_penalties": {
"pde":1,
"ic":20,
"bc":1
},
"verbose_out": false,
"show_plots": false
}
The script scripts/settings/freq_indep.json was used for training the Neumann model from the paper
{
"id": "freq_indep_sine_3_256_7sources_loss02",
"base_dir": "../data/pinn",
"c": 1,
"c_phys": 343,
"___COMMENT_fmax___": "2000Hz*c/343 = 5.8309 for c=1, =23.3236 for c=4",
"fmax": 5.8309,
"tmax": 4,
"xmin": -1,
"xmax": 1,
"source_pos": [-0.3,-0.2,-0.1,0.0,0.1,0.2,0.3],
"sigma0": 0.2,
"rho": 1.2,
"ppw": 5,
"epochs": 25000,
"stop_loss_value": 0.0002,
"batch_size": 512,
"learning_rate": 0.0001,
"optimizer": "adam",
"boundary_type": "IMPEDANCE_FREQ_INDEP",
"data_filename": "freq_indep_1D_2000.00Hz_sigma0.2_c1_xi5.83_srcs7.hdf5",
"__comment0__": "NN setting for the PDE",
"activation": "sin",
"num_layers": 3,
"num_neurons": 256,
"impedance_data": {
"__comment1__": "xi is the acoustic impedance ONLY for freq. indep. boundaries",
"xi": 5.83
},
"ic_points_distr": 0.25,
"bc_points_distr": 0.45,
"loss_penalties": {
"pde":1,
"ic":20,
"bc":1
},
"verbose_out": false,
"show_plots": false
}
The script scripts/settings/freq_dep.json was used for training the Neumann model from the paper
{
"id": "freq_dep_sine_3_256_7sources_d01",
"base_dir": "../data/pinn",
"c": 1,
"c_phys": 343,
"___COMMENT_fmax___": "2000Hz*c/343 = 5.8309 for c=1, =23.3236 for c=4",
"fmax": 5.8309,
"tmax": 4,
"xmin": -1,
"xmax": 1,
"source_pos": [-0.3,-0.2,-0.1,0.0,0.1,0.2,0.3],
"sigma0": 0.2,
"rho": 1.2,
"ppw": 5,
"epochs": 50000,
"stop_loss_value": 0.0002,
"do_transfer_learning": false,
"boundary_type": "IMPEDANCE_FREQ_DEP",
"data_filename": "freq_dep_1D_2000.00Hz_sigma0.2_c1_d0.10_srcs7.hdf5",
"batch_size": 512,
"learning_rate": 0.0001,
"optimizer": "adam",
"__comment0__": "NN setting for the PDE",
"activation": "sin",
"num_layers": 3,
"num_neurons": 256,
"__comment1__": "NN setting for the auxillary differential ODE",
"activation_ade": "tanh",
"num_layers_ade": 3,
"num_neurons_ade": 20,
"impedance_data": {
"d": 0.1,
"type": "IMPEDANCE_FREQ_DEP",
"lambdas": [7.1109025021758407,205.64002739443146],
"alpha": [6.1969460587749818],
"beta": [-15.797795759219973],
"Yinf": 0.76935257750377573,
"A": [-7.7594660571346719,0.0096108036858666163],
"B": [-0.016951521199665469],
"C": [-2.4690553703530442]
},
"accumulator_factors": [10.26, 261.37, 45.88, 21.99],
"ic_points_distr": 0.25,
"bc_points_distr": 0.45,
"loss_penalties": {
"pde":1,
"ic":20,
"bc":1,
"ade":[10,10,10,10]
},
"verbose_out": false,
"show_plots": false
}
HPC (DTU)
The scripts for training the models on the GPULAB clusters at DTU are located at scripts/settings/run_*.sh.
VSCode
Launch scripts for VS Code are located inside .vscode and running the settings script local_train.json in debug mode is done selecting the Python: TRAIN scheme (open pinn-acoustics.code-workspace to enable the workspace).
License
See LICENSE
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