Code for sound field predictions in domains with impedance boundaries. Used for generating results from the paper

Related tags

Deep Learningacousticsimpedance-boundary-conditionphysics-informed-neural-networks
Overview

Authors:

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

do_animations = do_side_by_side_plot = "> 
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

Owner
DTU Acoustic Technology Group
DTU Acoustic Technology Group
GitHub Repository
🔥3D-RecGAN in Tensorflow (ICCV Workshops 2017)

3D Object Reconstruction from a Single Depth View with Adversarial Learning Bo Yang, Hongkai Wen, Sen Wang, Ronald Clark, Andrew Markham, Niki Trigoni

Bo Yang 125 Nov 26, 2022
Learning Spatio-Temporal Transformer for Visual Tracking

STARK The official implementation of the paper Learning Spatio-Temporal Transformer for Visual Tracking Hiring research interns for visual transformer

Multimedia Research 484 Dec 29, 2022
CMUA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating Deepfakes (AAAI2022)

CMUA-Watermark The official code for CMUA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating Deepfakes (AAAI2022) arxiv. It is bas

50 Nov 26, 2022
PlenOctree Extraction algorithm

PlenOctrees_NeRF-SH This is an implementation of the Paper PlenOctrees for Real-time Rendering of Neural Radiance Fields. Not only the code provides t

49 Nov 05, 2022
利用Tensorflow实现基于CNN的中文短文本分类

Text Classification with CNN 使用卷积神经网络进行中文文本分类 CNN做句子分类的论文可以参看: Convolutional Neural Networks for Sentence Classification 还可以去读dennybritz大牛的博客：Implemen

Jeremiah 4 Nov 08, 2022
This project uses ViT to perform image classification tasks on DATA set CIFAR10.

Vision-Transformer-Multiprocess-DistributedDataParallel-Apex Introduction This project uses ViT to perform image classification tasks on DATA set CIFA

Kaicheng Yang 3 Jun 03, 2022
Pure python implementations of popular ML algorithms.

Minimal ML algorithms This repo includes minimal implementations of popular ML algorithms using pure python and numpy. The purpose of these notebooks

Alexis Gidiotis 3 Jan 10, 2022
A repository for interferometer controller code.

dses-interferometer-controller A repository for interferometer controller code, hardware, and simulations. See dses.science for more information on th

Eli Reed 1 Jan 17, 2022
Python based Advanced AI Assistant

Knick is a virtual artificial intelligence project, fully developed in python. The objective of this project is to develop a virtual assistant that can handle our minor, intermediate as well as heavy

19 Nov 15, 2022
This script runs neural style transfer against the provided content image.

Neural Style Transfer Content Style Output Description: This script runs neural style transfer against the provided content image. The content image m

Martynas Subonis 0 Nov 25, 2021
Official implementation of the paper Vision Transformer with Progressive Sampling, ICCV 2021.

Vision Transformer with Progressive Sampling This is the official implementation of the paper Vision Transformer with Progressive Sampling, ICCV 2021.

yuexy 123 Jan 01, 2023
A Domain-Agnostic Benchmark for Self-Supervised Learning

DABS: A Domain Agnostic Benchmark for Self-Supervised Learning This repository contains the code for DABS, a benchmark for domain-agnostic self-superv

Alex Tamkin 81 Dec 09, 2022
Link prediction using Multiple Order Local Information (MOLI)

Understanding the network formation pattern for better link prediction Authors: [e

Wu Lab 0 Oct 18, 2021
PyTorch implemention of ICCV'21 paper SGPA: Structure-Guided Prior Adaptation for Category-Level 6D Object Pose Estimation

SGPA: Structure-Guided Prior Adaptation for Category-Level 6D Object Pose Estimation This is the PyTorch implemention of ICCV'21 paper SGPA: Structure

Chen Kai 24 Dec 05, 2022
All public open-source implementations of convnets benchmarks

convnet-benchmarks Easy benchmarking of all public open-source implementations of convnets. A summary is provided in the section below. Machine: 6-cor

Soumith Chintala 2.7k Dec 30, 2022
An Empirical Investigation of Model-to-Model Distribution Shifts in Trained Convolutional Filters

CNN-Filter-DB An Empirical Investigation of Model-to-Model Distribution Shifts in Trained Convolutional Filters Paul Gavrikov, Janis Keuper Paper: htt

Paul Gavrikov 18 Dec 30, 2022
A real world application of a Recurrent Neural Network on a binary classification of time series data

What is this This is a real world application of a Recurrent Neural Network on a binary classification of time series data. This project includes data

Josep Maria Salvia Hornos 2 Jan 30, 2022
CNNs for Sentence Classification in PyTorch

Introduction This is the implementation of Kim's Convolutional Neural Networks for Sentence Classification paper in PyTorch. Kim's implementation of t

Shawn Ng 956 Dec 19, 2022
TAPEX: Table Pre-training via Learning a Neural SQL Executor

TAPEX: Table Pre-training via Learning a Neural SQL Executor The official repository which contains the code and pre-trained models for our paper TAPE

Microsoft 157 Dec 28, 2022
AlphaBot2 Pi Core software for interfacing with the various components.

AlphaBot2-Pi-Core AlphaBot2 Pi Core software for interfacing with the various components. This project is currently a W.I.P. I will update this readme

KyleDev 1 Feb 13, 2022