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2037 research outputs found
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dolfinx_eqlb v1.2.0
No full textThis library contains an add-on to FEniCSx enabling local flux equilibration strategies. The resulting H(div) conforming fluxes can be used for the construction of adaptive finite element solvers for the Poisson problem [5][8], elasticity [1][9] or poro-elasticity [2][10].
The equilibration process relies on so called patches, groups of all cells, connected with one node of the mesh. On each patch a constrained minimisation problem is solved [8]. In order to improve computational efficiency, a so called semi-explicit strategy [3][6] is also implemented. The solution procedure is thereby split into two steps: An explicit determination of an H(div) function, fulfilling the minimisation constraints, followed by an unconstrained minimisation on a reduced, patch-wise ansatz space. If equilibration is applied to elasticity - the stress tensor has a distinct symmetry - an additional constrained minimisation step after the row wise reconstruction of the tensor [1] is implemented.
[1] Bertrand, F., Kober, B., Moldenhauer, M. and Starke, G.: Weakly symmetric stress equilibration and a posteriori error estimation for linear elasticity. Comput. Math. Appl. (2021) doi: 10.1002/num.22741
[2] Bertrand, F. and Starke, G.: A posteriori error estimates by weakly symmetric stress reconstruction for the Biot problem. Numer. Methods Partial Differ. Equ. (2021) doi: 10.1016/j.camwa.2020.10.011
[3] Bertrand, F., Carstensen, C., Gräßle, B. and Tran, N.T.: Stabilization-free HHO a posteriori error control. Numer. Math. (2023) doi: 10.1007/s00211-023-01366-8
[4] Boffi, D., Brezzi, F. and Fortin, M.: Mixed finite element methods and applications. Springer Heidelberg, Berlin (2013).
[5] Braess, D. and Schöberl, J.: Equilibrated Residual Error Estimator for Edge Elements. Math. Comput. 77, 651-672 (2008)
[6] Cai, Z. and Zhang, S.: Robust equilibrated residual error estimator for diffusion problems: conforming elements. SIAM J. Numer. Anal. (2012). doi: 10.1137/100803857
[7] Kim, K.-Y.: Guaranteed A Posteriori Error Estimator for Mixed Finite Element Methods of Linear Elasticity with Weak Stress Symmetry. SIAM J. Numer. Anal. (2015) doi: 10.1137/110823031
[8] Ern, A and Vohralı́k, M.: Polynomial-Degree-Robust A Posteriori Estimates in a Unified Setting for Conforming, Nonconforming, Discontinuous Galerkin, and Mixed Discretizations. SIAM J. Numer. Anal. (2015) doi: 10.1137/130950100
[9] Prager, W. and Synge, J.L.: Approximations in elasticity based on the concept of function space. Q. J. Mech. Appl. Math. 5, 241-269 (1947)
[10] Riedlbeck, R., Di Pietro, D.A., Ern, A., Granet, S. and Kazymyrenko, K.: Stress and flux reconstruction in Biot’s poro-elasticity problem with application to a posteriori error analysis. Comput. Math. Appl. (2017) doi: 10.1016/j.camwa.2017.02.005</a
A Data Set for Research on Differential-Drive Mobile Robots in the Context of EDMD
No full textGeneral
This dataset contains real-world measurement data for data-based modeling of a differential-drive robot.
The dataset is especially tailored for data-based modeling using Extended Dynamic Mode Decomposition (EDMD) for control-affine systems.
It contains predecessor and successor pose data of the wheeled mobile robot (i.e., its position in the plane of an inertial frame of reference as well as its orientation w.r.t. the x-axis) when constant control inputs are applied to the robot, which is done for two different realizations of the differential-drive robot.
In the first realization, a desired constant translational and rotational velocity is sent to the robot (kinematic realization), while in the second realization, the robot's control actions are desired translational and rotational accelerations (second-order robot).
A total of three different datasets are provided, two for the kinematic mobile robot and one for the second-order robot.
The second, smaller dataset for the kinematic mobile robot shall indicate the data-efficiency of the EDMD approach.
For each of the three datasets, three raw data files with predecessor pose data (X_i.dat) and three raw data files of successor pose data (Y_i.dat) are provided, where the number i from the set {0,1,2} corresponds to the predecessor and successor data and indicates the applied control basis u_i.
In addition to zero control (i=0), the EDMD approach requires data for the differential-drive mobile robot for two linearly independent constant control vectors over a predefined sampling time.
Further information about the chosen control bases and the sampling times can be found in the readme files associated with the dataset directories.
Notably, the dataset for the second-order robot realization additionally contains approximative velocity data as well as the exact times at which the pose measurement of the external motion capture system has been received.
This additional time information is provided to facilitate the smoothing of the velocity data.
File Setup
The following files and directories are provided.
kinematic_dataset1
This directory contains raw data files containing the predecessor and successor pose data for the first sampling of the kinematic mobile robot. Each line consists of [x-position, y-position, orientation]. The chosen constant control vectors read u0=[0 m/s, 0 rad/s], u1=[0.2 m/s, 0.6 rad/s], and u2=[0.2 m/s, -0.4 rad/s] and the sampling time is 0.1 seconds.
kinematic_dataset2
This directory contains raw data files containing the predecessor and successor pose data for the second sampling of the kinematic mobile robot. Each line consists of [x-position, y-position, orientation]. The chosen constant control vectors read u0=[0 m/s, 0 rad/s], u1=[0.2 m/s, 0.6 rad/s], and u2=[0.2 m/s, -0.4 rad/s] and the sampling time is 0.05 seconds.
secondorder_dataset
This directory contains raw data files containing the predecessor and successor pose data for the sampling of the second-order mobile robot. Each line consists of [x-position, y-position, orientation, v (translational velocity), omega (angular velocity)]. The chosen constant control vectors read u0=[0 m/^2s, 0 rad/s^2], u1=[0.2 m/s^2, 0 rad/s^2], and u2=[0 m/s^2, 0.5 rad/s^2] and the sampling time is 0.05s. Note that additional time instances of the measured data are provided in the respective first column. This might facilitate the necessary smoothing of the translational and angular velocities.
ProcessVisualizeKinematic.m
This is a minimal MATLAB file which can be used to process and visualize the recorded data for the kinematic mobile robot. Further information can be found in the comments of the file.
ProcessVisualizeSecondorder.m
This is a minimal MATLAB file which can be used to process and visualize the recorded data for the second-order mobile robot. Further information can be found in the comments of the file.
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Code and Data for: Better by default: Strong pre-tuned MLPs and boosted trees on tabular data [NeurIPS, arXiv v2]
No full textThis dataset contains code and data for our paper "Better by default: Strong pre-tuned MLPs and boosted trees on tabular data", specifically, the NeurIPS version which is also the second version on arXiv. The main code is provided in pytabkit_code.zip and contains further documentation in README.md and the docs folder. The main code is also provided on GitHub. Here, we additionally provide the data that is generated by the code as well as the plots. See the documentation in docs/source/bench/download_results.md in the main code for instructions on how/when to download which data, or the documentation hosted here. The code for the old version of the Grinsztajn et al. (2022) benchmark is provided in grinsztajn_benchmarking_code.zip and on GitHub. The code and data for the first arXiv version of the paper are archived here
Replication Data for: "Structural Characterization of Surface Immobilized Platinum Hydrides by Sensitivity-Enhanced 195Pt Solid State NMR Spectroscopy and DFT Calculations"
No full textAll primary data files of measurements and processed data of the journal article mentioned under related publications from Estes group can be found here. Data from collaborating groups can be found in a seperate data set. The data is structured according to figures and schemes in the research article and contains the following data types: NMR (mestrenova, bruker), IR (.csv), pxrd (.csv
FabFramework.Core: Task-Based Robotic Fabrication Framework
No full textFabFramework is designed around a Task-Based Fabrication methodology, which transforms digital design models into sequential, data-driven robotic tasks. This approach ensures precise fabrication operations through simulation and predefined parameters, following a three-step process.
The framework begins with the Design layer, where the user design model is converted into a model consisting of beams and plates with defined geometric attributes. The Fab layer builds on this by adjusting these elements to fit specific fabrication requirements, such as positioning, assembly sequence, and alignment within the robotic workspace. This layer also accommodates unique connection methods and robotic adjustments. Finally, the Task layer sequences the robot’s actions—like picking, placing, and tool changes—to streamline assembly, with frame-based tasks for precise movements and non-frame-based tasks for simpler actions.
FabFramework is setup to be integrated with the ICD Virtual Robot Grasshopper Plugin, allowing users to simulate and verify the entire fabrication process digitally before fabrication
Data repository for "Joint Wire Cutting with Non-Maximally Entangled States"
No full textThis dataset contains the replication code for the publication titled "Joint Wire Cutting with Non-Maximally Entangled States." The provided code represents the version utilized to generate the experimental results documented in the corresponding publication.
The experiments investigate the influence of entangled states in a joint wire cut using different configurations.
For comprehensive instructions on using the provided data and code, please refer to the README.md file.
To reproduce the plots from the paper, execute the Jupyter notebook plot.ipynb with the data available in data_exp_1.csv and data_exp_2.csv.
To generate new data, utilize the experiments.ipynb.
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Replication Data for: Anisotropic Damping and Stiffness of laminated steel parts using adhesive bonding - an empirical influence study
No full textThis dataset contains all experimental data that is shown within the paper "Anisotropic Damping and Stiffness of laminated steel parts using adhesive
bonding - an empirical influence study".
Bending beams manufactured by Layer Laminated Manufacturing (LLM) were investigated in order to analyze the influence of different manufacturing parameters on the resulting part properties. For each investigated parameter combination, three redundant specimens are manufactured and examined.
Experimental modal analysis (EMA) and static bending test are used as examination methods in order to obtain the eigenfrequencies, the modal dampings and the bending stiffness of the beams.
The data is summarized within the file data.xlsx, which contains the following spreadsheets:
EMA: Eigenfrequencies and modal dampings of the first three eigenmodes for all investigated specimens, identified by experimental modal analysis.
EMA - mean: Mean eigenfrequencies and modal dampings of the first three eigenmodes for all investigated parameter combinations, averaged over three redundant specimens that were manufactured for each parameter combination
bending: Displacements for three different applied weights (5, 10, 15 kg) and the resulting bending stiffness for each specimen
bending - mean: Mean displacements for the three different weights and the resulting bending stiffness for each parameter combination.
ratios normal-parallel: As a anisotropy measure, all part properties normal and parallel to the slicing direction are set in relation to each other.
parameter combinations: Nomenclature for the parameter combinations used within the study
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D1244 sensor data (November 2023)
No full text
General information:
This dataset contains measurements from the adaptive high-rise demonstrator building D1244, built in the scope of the CRC1244. This 36m high building is equipped with 24 hydraulic actuators providing the basis for its structural adaptation. Strain gauges, pressure sensors and position encoders are mounted throughout the building and used for state estimation and monitoring.
Structure of the dataset:
Each zip-file contains measurements of one day in the hdf5 format.
The hdf5-files in each zip-file contain an array of 244 signals sampled over 10^6 time steps at approximately 200Hz.
labels.csv contains auxiliary information on all measured signals, including the sensor type and the sensor's location in the building
File contents:
Each hdf5-file contains signals of the following types, arranged as stated in labels.csv:
strain: strain (in mm/m) in columns and diagonal bracing elements, measured by strain gauges.
pressure: pressure (in bar) in the piston side or the rod side chamber of a hydraulic actuator. For actuators in the diagonal bracing, the rod side chamber is permanently connected to the tank.
posenc: displacement (in meters) of each actuator, measured by a position encoder.
optic: optically measured displacement (in meters) of emitters attached to the building's facade.
The building consists of four modules spanning three stories each, and all sensors within a module are connected to a control cabinet, from which all measurements are transmitted. The cameras of the optical measurement system are placed outside the building and transmit their data separately from the sensors within the building. Therefore, there are an additional two signals per module (or camera):
timestamp: unix timestamp (seconds since 1st January 1970) of the control cabinet
numvars: number of measured variables
Missing measurements are marked as NaN. The optical measurement system is currently undergoing maintenance, which is why the corresponding signals are all NaN.
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ABxM.DistributedRobotics.RADr: Agent-based Design and Control of multiple Roaming Autonomous Distributed robots (RADr)
No full textABxM.DistributedRobotics.RADr is an add-on to ABxM.Core for agent-based design and control of multiple Roaming Autonomous Distributed robots (RADr) that assemble hexagonal digital materials. The add-on contains various agent system constructs and utilities for simulation of the swarm within Rhino/Grasshopper and control of physical swarm of wheeled mobile robots. This version contains the tools for MQTT communication between Rhino/Grasshopper and the robots as well as between Rhino/Grasshopper and Motive, the software from Optitrack a motion capture system that can be utilized to track the robots. MQTT is a standard messaging protocol for the Internet of Things (IoT).The package includes two example files:
01_Example_CollaborativeCarrying.gh: Simulates a swarm of robots that must collaborate to move the digital material
02_Example_PaintedDesert.gh: Simulates the sorting of digital material by color. </li
Quanser IP-02 Inverted Cartpole Measurement Data for Analysis and Offline RL
No full textRecorded Data of the Swingup Task on the Inverted Cartpole IP-02 by Quanser.
State transitions are as following:
(pos, pos_dot, phi, phi_dot, action) -> (pos_new, pos_dot_new, phi_new, phi_dot_new)
Actions are discrete from 0 to 8, with 4 being the zero-action. I.e. 0 and 8 are full force in either direction