PN 3-5

SimTech Project PN 3-5 "Data-integrated Multiscale Modeling of Diffusion-driven Processes in Porous Media" and SimTech Project PN 3-5 (II) "Data-driven multi-scale stability analysis of multi-stimuli-responsive hydrogels"

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1 to 10 of 30 Results

Optimization of magnetoelectric composites using convolutional neural networks - scripts and dataset Dec 22, 2025 Sriram, Siddharth, 2025, "Optimization of magnetoelectric composites using convolutional neural networks - scripts and dataset", https://doi.org/10.18419/DARUS-5623, DaRUS, V1 This repository is intended to host the supplementary material (dataset and scripts) associated with the article “Optimization of magnetoelectric composites using convolutional neural networks”. The HDF5 file 'Microstructure_data.h5' contains the dataset of microstructural images and their effective properties of interest, computed using the finite...
Effective_piezo-coefficients_training-from-scratch.tar.xz Dec 22, 2025 - Optimization of magnetoelectric composites using convolutional neural networks - scripts and dataset XZ Archive - 148.7 MB - MD5: c555cdad857f4f12fe4e0d1e45d240e2
Effective_piezo-coefficients_transfer-learning_fine-tuning.tar.xz Dec 22, 2025 - Optimization of magnetoelectric composites using convolutional neural networks - scripts and dataset XZ Archive - 348.6 MB - MD5: 5b39acde422e7818b0c656216ec98703
Effective_remnant_polarization.tar.xz Dec 22, 2025 - Optimization of magnetoelectric composites using convolutional neural networks - scripts and dataset XZ Archive - 148.6 MB - MD5: 1bdeaee6b04b9457f166deea6022a6c6
Explainability_analysis.tar.xz Dec 22, 2025 - Optimization of magnetoelectric composites using convolutional neural networks - scripts and dataset XZ Archive - 148.5 MB - MD5: e442e1be178be052b48cbee3b7c7804c
Microstructure_data.h5 Dec 22, 2025 - Optimization of magnetoelectric composites using convolutional neural networks - scripts and dataset Hierarchical Data Format - 972.1 MB - MD5: a37eb6a8912e2e90c95b46854487dee4
README.pdf Dec 22, 2025 - Optimization of magnetoelectric composites using convolutional neural networks - scripts and dataset Adobe PDF - 77.2 KB - MD5: d4c60b8e903ce04c1ce240c8abea5747
Data-driven analysis of structural instabilities in electroactive polymer bilayers based on a variational saddle-point principle: Datasets and ML codes Feb 16, 2024 - PN3-5 Sriram, Siddharth, 2024, "Data-driven analysis of structural instabilities in electroactive polymer bilayers based on a variational saddle-point principle: Datasets and ML codes", https://doi.org/10.18419/DARUS-3881, DaRUS, V1 The datasets and codes provided here are associated with our article entitled "Data-driven analysis of structural instabilities in electroactive polymer bilayers based on a variational saddle-point principle". The main idea of the work is to develop surrogate models using the concepts of machine learning (ML) to predict the onset of wrinkling insta...
binary_classification.ipynb Feb 16, 2024 - Data-driven analysis of structural instabilities in electroactive polymer bilayers based on a variational saddle-point principle: Datasets and ML codes Jupyter Notebook - 13.7 KB - MD5: dacad485cece6350acf08afa30461e19
de_instabilities.yml Feb 16, 2024 - Data-driven analysis of structural instabilities in electroactive polymer bilayers based on a variational saddle-point principle: Datasets and ML codes application/x-yaml - 8.5 KB - MD5: 86f5d2848b4668165c8e936214efed7f

Optimization of magnetoelectric composites using convolutional neural networks - scripts and dataset

Dec 22, 2025

Sriram, Siddharth, 2025, "Optimization of magnetoelectric composites using convolutional neural networks - scripts and dataset", https://doi.org/10.18419/DARUS-5623, DaRUS, V1

This repository is intended to host the supplementary material (dataset and scripts) associated with the article “Optimization of magnetoelectric composites using convolutional neural networks”. The HDF5 file 'Microstructure_data.h5' contains the dataset of microstructural images and their effective properties of interest, computed using the finite...

Effective_piezo-coefficients_training-from-scratch.tar.xz

Dec 22, 2025 - Optimization of magnetoelectric composites using convolutional neural networks - scripts and dataset

XZ Archive - 148.7 MB -

Effective_piezo-coefficients_transfer-learning_fine-tuning.tar.xz

Dec 22, 2025 - Optimization of magnetoelectric composites using convolutional neural networks - scripts and dataset

XZ Archive - 348.6 MB -

Effective_remnant_polarization.tar.xz

Dec 22, 2025 - Optimization of magnetoelectric composites using convolutional neural networks - scripts and dataset

XZ Archive - 148.6 MB -

Explainability_analysis.tar.xz

Dec 22, 2025 - Optimization of magnetoelectric composites using convolutional neural networks - scripts and dataset

XZ Archive - 148.5 MB -

Microstructure_data.h5

Dec 22, 2025 - Optimization of magnetoelectric composites using convolutional neural networks - scripts and dataset

Hierarchical Data Format - 972.1 MB -

README.pdf

Dec 22, 2025 - Optimization of magnetoelectric composites using convolutional neural networks - scripts and dataset

Adobe PDF - 77.2 KB -

Data-driven analysis of structural instabilities in electroactive polymer bilayers based on a variational saddle-point principle: Datasets and ML codes

Feb 16, 2024 - PN3-5

Sriram, Siddharth, 2024, "Data-driven analysis of structural instabilities in electroactive polymer bilayers based on a variational saddle-point principle: Datasets and ML codes", https://doi.org/10.18419/DARUS-3881, DaRUS, V1

The datasets and codes provided here are associated with our article entitled "Data-driven analysis of structural instabilities in electroactive polymer bilayers based on a variational saddle-point principle". The main idea of the work is to develop surrogate models using the concepts of machine learning (ML) to predict the onset of wrinkling insta...

binary_classification.ipynb

Feb 16, 2024 - Data-driven analysis of structural instabilities in electroactive polymer bilayers based on a variational saddle-point principle: Datasets and ML codes

Jupyter Notebook - 13.7 KB -

de_instabilities.yml

Feb 16, 2024 - Data-driven analysis of structural instabilities in electroactive polymer bilayers based on a variational saddle-point principle: Datasets and ML codes

application/x-yaml - 8.5 KB -

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