32 machine learning datasets
32 dataset results
This dataset contains simulated synthetic particle decays, simulated using the PhaseSpace library. All simulated decay topologies have a common root particle of mass 100 (arbitrary units). Intermediate particles are selected at random with replacement from the following masses: [90, 80, 70, 50, 25, 20, 10]. Final state particles, which make up the leaf nodes of generated topologies, are drawn with replacement from the following masses: [1, 2, 3, 5, 12]. For each intermediate particle (including the root), we limit the minimum number of children to two, and the maximum five. The dataset contains the resulting simulated particle physics decays, with information about the detected particle (leaves) to be used as input, and Lowest Common Ancestor Generations (LCAGs) to be used as training targets.
Dataset of low fidelity resolutions of the RANS equations over airfoils.
This paper details the design of an autonomous alignment and tracking platform to mechanically steer directional horn antennas in a sliding correlator channel sounder setup for 28 GHz V2X propagation modeling. A pan-and-tilt subsystem facilitates uninhibited rotational mobility along the yaw and pitch axes, driven by open-loop servo units and orchestrated via inertial motion controllers. A geo-positioning subsystem augmented in accuracy by real-time kinematics enables navigation events to be shared between a transmitter and receiver over an Apache Kafka messaging middleware framework with fault tolerance. Herein, our system demonstrates a 3D geo-positioning accuracy of 17 cm, an average principal axes positioning accuracy of 1.1 degrees, and an average tracking response time of 27.8 ms. Crucially, fully autonomous antenna alignment and tracking facilitates continuous series of measurements, a unique yet critical necessity for millimeter wave channel modeling in vehicular networks. The
This dataset provides neutron and gamma-ray pulse signals for pulse shape discrimination experiments. Serval traditional and recently proposed pulse shape discrimination algorithms are utilized to conduct pulse shape discrimination under raw pulse signals and noise-enhanced datasets. These algorithms include zero-crossing (ZC), charge comparison (CC), falling edge percentage slope (FEPS), frequency gradient analysis (FGA), pulse-coupled neural network (PCNN), ladder gradient (LG), and heterogeneous quasi-continuous spiking cortical model (HQC-SCM). This dataset also provides the source code of all these pulse shape discrimination methods, together with the source code of schematic pulse shape discrimination performance evaluation and anti-noise performance evaluation.
Simulation data and pre-trained Graph Neural Network (GNN) models produced in [1].
This repository contains data for the NeurIPS conference paper titled "Harnessing Machine Learning for Single-Shot Measurement of Free Electron Laser Pulse Power".
Structured atmospheric data for AI/ML Long-term, pre-processed, atmospheric datasets for use in Machine Learning/AI based forecasting. Initially intended to predict AOD, however can be adapted for prediction of other atmospheric particles.
This dataset contains over 47,000 LEGO structures of over 28,000 unique 3D objects accompanied by detailed captions. It was used to train LegoGPT, the first approach for generating physically stable LEGO brick models from text prompts.
SynD is a synthetic energy dataset with a focus on residential buildings. This dataset is the result of a custom simulation process that relies on power traces of household appliances. The output of simulations is the power consumption of 21 household appliances as well as the household-wide consumption (i.e. mains). Therefore, SynD's can be used for Non-Intrusive Load Monitoring, also referred to as Energy Disaggregation.
Laser powder bed fusion (LBPF) is the additive manufacturing (3D printing) process for metals. RAISE-LPBF is a large dataset on the effect of laser power and laser dot speed in 316L stainless steel bulk material. Both process parameters are independently sampled for each scan line from a continuous distribution, so interactions of different parameter choices can be investigated. Process monitoring comprises on-axis high-speed (20k FPS) video. The data can be used to derive statistical properties of LPBF, as well as to build anomaly detectors.
Data Set Structure Fluid Structure Interaction(NS +Elastic wave) The TF_fsi2_results folder contains simulation data organized by various parameters (mu, x1, x2) where mu determines the viscosity and x1 and x2 are the parameters of the inlet condition. The dataset includes files for mesh, displacement, velocity, and pressure.
This record contains the saddle search output logs for Sella and EON (dimer, with and without GPR acceleration). The data also includes full trajectories of the GPR accelerated dimer method for visual analysis. These logs are used to generate the figures in the manuscript. For details, refer to the code in the associated GitHub repository.