21 to 30 of 34 Results
May 15, 2023 - PN 4-7
Baier, Alexandra; Frank, Daniel, 2023, "deepsysid: System Identification Toolkit for Multistep Prediction using Deep Learning", https://doi.org/10.18419/darus-3455, DaRUS, V1
deepsysid is a system identification toolkit for multistep prediction using deep learning and hybrid methods. The toolkit is easy to use. After you follow the instructions in the README, you will be able to download a dataset, run hyperparameter optimization and identify your bes... |
May 15, 2023 - PN 4-7
Baier, Alexandra; Aspandi, Decky; Staab, Steffen, 2023, "Supplements for "ReLiNet: Stable and Explainable Multistep Prediction with Recurrent Linear Parameter Varying Networks""", https://doi.org/10.18419/darus-3457, DaRUS, V1
This repository contains the necessary scripts to reproduce the results from our paper "ReLiNet: Stable and Explainable Multistep Prediction with Recurrent Linear Parameter Varying Networks". See the README file for more information. The most current version of this software is a... |
Tapia Camú, Cristóbal, 2023, "Replication Data for: Holes in glulam - Orientation and design of internal reinforcements", https://doi.org/10.18419/darus-3440, DaRUS, V1
This repository contains the finite element models for Abaqus used to generate the results shown in the paper. The models are written in Python and were tested with Abaqus v2016. Three models are provided: Model of a glulam beam with a circular hole without reinforcement Model of... |
Nov 7, 2022 - Institut für Strahlwerkzeuge
Hagenlocher, Christian, 2022, "Python implementation of heat accumulation equations for additive manufacturing", https://doi.org/10.18419/darus-2609, DaRUS, V1
This is a python implementation of the heat accumulation equations, which describe the increase of the residual temperature in additive manufacturing. The Derivation of the equations is described in the related paper given below. If you run the script in the console, it creates t... |
Oct 12, 2022 - Institute for Structural Mechanics
Krake, Tim; von Scheven, Malte, 2022, "Matlab Implementation of Efficient Updates of Redundancy Matrices", https://doi.org/10.18419/darus-2870, DaRUS, V1
This is a Demo for the manuscript 'Efficient Update of Redundancy Matrices for Truss and Frame Structures' that demonstrates the speedup and accuracy of the proposed update formulas. The computation is done in single-precision. Please open and run the main file. Further informati... |
Aug 8, 2022 - SFB-TRR 161 A01 "Uncertainty Quantification and Analysis in Visual Computing"
Hägele, David; Krake, Tim, 2022, "Source Code for Uncertainty-Aware Multidimensional Scaling", https://doi.org/10.18419/darus-2995, DaRUS, V1
This dataset contains the source code for the uncertainty-aware multidimensional scaling (UAMDS) algorithm implemented in the Java programming language. UAMDS is a nonlinear dimensionality reduction technique for sets of random vectors. The implemented UAMDS model allows to proje... |
Jul 20, 2022 - SFB 1333 C6 - Holm group, ICP
Tischler, Ingo; Holm, Christian; Schlaich, Alexander, 2022, "Publication data for: "The presence of a wall enhances the probability for ring-closing metathesis: insights from classical polymer theory and atomistic simulations - data from C6 group"", https://doi.org/10.18419/darus-1371, DaRUS, V1
All primary data files and processed data of the journal article from C6 group. It contains the simulation and analysis scripts, the ESPResSo commit and a patch to it, with which the data was produced. |
Tapia Camú, Cristóbal; Claus, Marian, 2022, "Replication Models for: A finger-joint based edge connection for the weak direction of CLT plates", https://doi.org/10.18419/darus-1259, DaRUS, V1
This repository contains the implementation of the analytical and numerical models described and used in the paper to model the developed edge connection for CLT plates in the weak direction. The connection was developed within the frame of the cluster "Integrative Computational... |
Jan 26, 2022 - PN 6-4
Munz, Tanja; Väth, Dirk; Kuznecov, Paul; Vu, Ngoc Thang; Weiskopf, Daniel, 2022, "NMTVis - Extended Neural Machine Translation Visualization System", https://doi.org/10.18419/darus-2124, DaRUS, V1
NMTVis is a web-based visual analytics system to analyze, understand, and correct translations generated with neural machine translation. First, a document can be translated using a neural machine translation model (we support an LSTM-based and the Transformer architecture). Afte... |
Zaverkin, Viktor; Holzmüller, David; Steinwart, Ingo; Kästner, Johannes, 2021, "Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments", https://doi.org/10.18419/darus-2136, DaRUS, V1
Code and documentation for the improved Gaussian Moments Neural Network (GM-NN). An updated version can be found on GitLab |
