121 to 130 of 278 Results
Mar 7, 2024 - Usability and Sustainability of Simulation Software
Kschidock, Helena, 2024, "Replication Data for: Development of a framework for point-particle tracking", https://doi.org/10.18419/DARUS-3760, DaRUS, V1
This dataset contains a custom build for Altair's simulation software ultraFluidX and the input files used in the Master Thesis 'Development of an Euler-Lagrangian framework for point-particle tracking to enable efficient multiscale simulations of complex flows', submitted by Helena Kschidock on 02/11/2023. For further details on the input files, a... |
Feb 26, 2024 - Modelling, Simulation and Optimization for Agonist-Antagonist Myoneural Interface Surgeries
Homs Pons, Carme; Lautenschlager, Robin, 2024, "Replication Data for: Coupled Simulations and Parameter Inversion for Neural System and Electrophysiological Muscle Models", https://doi.org/10.18419/DARUS-4031, DaRUS, V1
This dataset allows to reproduce the results from the paper "Coupled Simulations and Parameter Inversion for Neural System and Electrophysiological Muscle Models" submitted to GAMM Mitteilungen in September 2023. Find more information about the structure of the dataset and the steps to reproduce the results in the readme.md file. |
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... |
Feb 13, 2024 - SciML PDE Benchmark
Takamoto, Makoto; Praditia, Timothy; Leiteritz, Raphael; MacKinlay, Dan; Alesiani, Francesco; Pflüger, Dirk; Niepert, Mathias, 2022, "PDEBench Datasets", https://doi.org/10.18419/DARUS-2986, DaRUS, V8
This dataset contains benchmark data, generated with numerical simulation based on different PDEs, namely 1D advection, 1D Burgers', 1D and 2D diffusion-reaction, 1D diffusion-sorption, 1D, 2D, and 3D compressible Navier-Stokes, 2D Darcy flow, and 2D shallow water equation. This dataset is intended to progress the scientific ML research area. In ge... |
Jan 11, 2024 - Projects without PN Affiliation
Magiera, Jim M., 2024, "Replication Data for: Constraint-aware neural networks for Riemann problems", https://doi.org/10.18419/DARUS-3869, DaRUS, V1
Data sets of the article "Constraint-aware neural networks for Riemann problems", consisting of training and test data sets for Riemann solutions of the cubic flux model, an isothermal two-phase model, and the Euler equations for an ideal gas. You can find detailed information in the README.md. |
Dec 22, 2023 - SFB 1333 C6 - Holm group, ICP
Tischler, Ingo; Schlaich, Alexander; Holm, Christian, 2023, "Replication data of C6 group for: "Disentanglement of surface and confinement effects for diene metathesis in mesoporous confinement"", https://doi.org/10.18419/DARUS-3642, DaRUS, V1
The simulation scripts and the simulation data of the journal article mentioned under related publications from C6 group can be found here. The data is structured according to figures and schemes in the research article and contains the following data types: compressed (tar) text data files and python scripts. The structure within the tar file is a... |
Dec 19, 2023 - Projects without PN Affiliation
Rettberg, Johannes; Wittwar, Dominik; Herkert, Robin, 2023, "Softwarepackage CCMOR2", https://doi.org/10.18419/DARUS-3839, DaRUS, V1
The dataset entails the code of the CCMOR2 package developed in Matlab. This project aims to model and perform certified model order reduction on multi-physical systems. The systems are formulated in the port-Hamiltonian framework which incorporates useful system theoretic properties such as stability and passivity. The approach is subvided into th... |
Dec 15, 2023 - PN 2-3B
Santana Chacon, Pablo Filipe; Hammer, Maria; Wochner, Isabell; Walter, Johannes R.; Schmitt, Syn, 2023, "Replication Data for: A physiologically enhanced muscle spindle model: using a Hill-type model for extrafusal fibers as template for intrafusal fibers", https://doi.org/10.18419/DARUS-3796, DaRUS, V1
This code/data allows you reproduce the results of the paper: "A physiologically enhanced muscle spindle model: using a Hill-type model for extrafusal fibers as template for intrafusal fibers" by P. F. S. Chacon, M. Hammer, I. Wochner, J. R. Walter and S. Schmitt. Always cite the paper together with this dataset because this dataset is not self-exp... |
Dec 14, 2023 - Materials Design
Jung, Jong Hyun; Forslund, Axel; Srinivasan, Prashanth; Grabowski, Blazej, 2023, "Data for: Dynamically stabilized phases with full ab initio accuracy: Thermodynamics of Ti, Zr, Hf with a focus on the hcp-bcc transition", https://doi.org/10.18419/DARUS-3582, DaRUS, V1, UNF:6:PcXLVWUQ0T4geRQy0F0sgg== [fileUNF]
Data for the publication, Dynamically stabilized phases with full ab initio accuracy: Thermodynamics of Ti, Zr, Hf with a focus on the hcp-bcc transition, Phys. Rev. B 108, 184107 (2023). This data set contains 1) - the training sets (VASP files), - the low moment-tensor-potentials (MTPs) and high-MTPs (for seperate hcp and bcc phases and combined... |
Nov 30, 2023 - SciML PDE Benchmark
Takamoto, Makoto; Praditia, Timothy; Leiteritz, Raphael; MacKinlay, Dan; Alesiani, Francesco; Pflüger, Dirk; Niepert, Mathias, 2022, "PDEBench Pretrained Models", https://doi.org/10.18419/DARUS-2987, DaRUS, V2
This dataset contains the pretrained baseline models, namely FNO, U-Net, and PINN. These models are trained on different PDEs, such as 1D advection, 1D Burgers', 1D and 2D diffusion-reaction, 1D diffusion-sorption, 1D, 2D, and 3D compressible Navier-Stokes, 2D Darcy flow, and 2D shallow water equation. In addition the dataset contains the pre-train... |
