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
Supplemental learning materials for "Fourier Feature Networks and Neural Volume Rendering"

Fourier Feature Networks and Neural Volume Rendering This repository is a companion to a lecture given at the University of Cambridge Engineering Depa

Matthew A Johnson 133 Dec 26, 2022
Repo for the Video Person Clustering dataset, and code for the associated paper

Video Person Clustering Repo for the Video Person Clustering dataset, and code for the associated paper. This reporsitory contains the Video Person Cl

Andrew Brown 47 Nov 02, 2022
Regularizing Generative Adversarial Networks under Limited Data (CVPR 2021)

Regularizing Generative Adversarial Networks under Limited Data [Project Page][Paper] Implementation for our GAN regularization method. The proposed r

Google 148 Nov 18, 2022
Official implementation for "QS-Attn: Query-Selected Attention for Contrastive Learning in I2I Translation" (CVPR 2022)

QS-Attn: Query-Selected Attention for Contrastive Learning in I2I Translation (CVPR2022) https://arxiv.org/abs/2203.08483 Unpaired image-to-image (I2I

Xueqi Hu 50 Dec 16, 2022
Udacity Suse Cloud Native Foundations Scholarship Course Walkthrough

SUSE Cloud Native Foundations Scholarship Udacity is collaborating with SUSE, a global leader in true open source solutions, to empower developers and

Shivansh Srivastava 34 Oct 18, 2022
Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks

SSTNet Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks(ICCV2021) by Zhihao Liang, Zhihao Li, Songcen Xu, Mingkui Tan, Kui J

83 Nov 29, 2022
Implementation of ResMLP, an all MLP solution to image classification, in Pytorch

ResMLP - Pytorch Implementation of ResMLP, an all MLP solution to image classification out of Facebook AI, in Pytorch Install $ pip install res-mlp-py

Phil Wang 178 Dec 02, 2022
All supplementary material used by me while TA-ing CS3244: Machine Learning

CS3244-Tutorial-Material All supplementary material used by me while TA-ing CS3244: Machine Learning at NUS School of Computing. What is this? I teach

Rishabh Anand 18 Sep 23, 2022
Awesome Remote Sensing Toolkit based on PaddlePaddle.

基于飞桨框架开发的高性能遥感图像处理开发套件，端到端地完成从训练到部署的全流程遥感深度学习应用。 最新动态 PaddleRS 即将发布alpha版本！欢迎大家试用 简介 PaddleRS是遥感科研院所、相关高校共同基于飞桨开发的遥感处理平台，支持遥感图像分类，目标检测，图像分割，以及变化检测等常用遥

146 Dec 11, 2022
Lightwood is Legos for Machine Learning.

Lightwood is like Legos for Machine Learning. A Pytorch based framework that breaks down machine learning problems into smaller blocks that can be glu

MindsDB Inc 312 Jan 08, 2023
Convert BART models to ONNX with quantization. 3X reduction in size, and upto 3X boost in inference speed

fast-Bart Reduction of BART model size by 3X, and boost in inference speed up to 3X BART implementation of the fastT5 library (https://github.com/Ki6a

Siddharth Sharma 19 Dec 09, 2022
Improving Factual Completeness and Consistency of Image-to-text Radiology Report Generation

Improving Factual Completeness and Consistency of Image-to-text Radiology Report Generation The reference code of Improving Factual Completeness and C

46 Dec 15, 2022
OREO: Object-Aware Regularization for Addressing Causal Confusion in Imitation Learning (NeurIPS 2021)

OREO: Object-Aware Regularization for Addressing Causal Confusion in Imitation Learning (NeurIPS 2021) Video demo We here provide a video demo from co

20 Nov 25, 2022
Convert openmmlab (not only mmdetection) series model to tensorrt

MMDet to TensorRT This project aims to convert the mmdetection model to TensorRT model end2end. Focus on object detection for now. Mask support is exp

JinTian 4 Dec 17, 2021
SuRE Evaluation: A Supplementary Material

SuRE Evaluation: A Supplementary Material This repository contains supplementary material regarding the evaluations presented in the paper Visual Expl

NYU Visualization Lab 0 Dec 14, 2021
Unofficial implementation of PatchCore anomaly detection

PatchCore anomaly detection Unofficial implementation of PatchCore(new SOTA) anomaly detection model Original Paper : Towards Total Recall in Industri

Changwoo Ha 268 Dec 22, 2022
🎁 3,000,000+ Unsplash images made available for research and machine learning

The Unsplash Dataset The Unsplash Dataset is made up of over 250,000+ contributing global photographers and data sourced from hundreds of millions of

Unsplash 2k Jan 03, 2023
ElasticFace: Elastic Margin Loss for Deep Face Recognition

This is the official repository of the paper: ElasticFace: Elastic Margin Loss for Deep Face Recognition Paper on arxiv: arxiv Model Log file Pretrain

Fadi Boutros 113 Dec 14, 2022
Taichi Course Homework Template

太极图形课S1-标题部分 这个作业未来或将是你的开源项目，标题的内容可以来自作业中的核心关键词，让读者一眼看出你所完成的工作/做出的好玩demo 如果暂时未想好，起名时可以参考“太极图形课S1-xxx作业” 如下是作业（项目）展开说明的方法，可以帮大家理清思路，并且也对读者非常友好，请小伙伴们多多参

TaichiCourse 30 Nov 19, 2022
DFM: A Performance Baseline for Deep Feature Matching

DFM: A Performance Baseline for Deep Feature Matching Python (Pytorch) and Matlab (MatConvNet) implementations of our paper DFM: A Performance Baselin

143 Jan 02, 2023