471 skills found · Page 1 of 16
gravitational / WorkshopDocker, Kubernetes and Gravity Trainings by Gravitational
mockingbirdnest / Principia𝑛-Body and Extended Body Gravitation for Kerbal Space Program
cliffcrosland / Black Hole.jsRenders black hole gravitational lensing effects in an image canvas using WebGL, glfx.js, and numeric.js
gwastro / PycbcCore package to analyze gravitational-wave data, find signals, and study their parameters. This package was used in the first direct detection of gravitational waves (GW150914), and is used in the ongoing analysis of LIGO/Virgo data.
lesgourg / Class PublicPublic repository of the Cosmic Linear Anisotropy Solving System (master for the most recent version of the standard code; GW_CLASS to include Cosmic Gravitational Wave Background anisotropies; classnet branch for acceleration with neutral networks; ExoCLASS branch for exotic energy injection; class_matter branch for FFTlog)
Canleskis / Ephemeris ExplorerA simulator of gravitationally bound systems.
sxs-collaboration / SpectreSpECTRE is a code for multi-scale, multi-physics problems in astrophysics and gravitational physics.
PyAutoLabs / PyAutoLensPyAutoLens: Open-Source Strong Gravitational Lensing
hhyyti / Dcm ImuThe DCM-IMU algorithm is designed for fusing low-cost triaxial MEMS gyroscope and accelerometer measurements. An extended Kalman filter is used to estimate attitude in direction cosine matrix (DCM) formation and gyroscope biases online. A variable measurement covariance method is implemented for acceleration measurements to ensure robustness against temporarily non-gravitational accelerations which usually induce errors to attitude estimate in ordinary IMU-algorithms. The code and data will be added after related scientific work is published and open source publication is approved.
adrn / GalaGalactic and gravitational dynamics in Python
gwastro / PyCBC TutorialsLearn how to use PyCBC to analyze gravitational-wave data and do parameter inference.
iphysresearch / GWData BootcampGravitational Wave Data Exploration: A Practical Training in Programming and Analysis
bilby-dev / BilbyA unified framework for stochastic sampling packages and gravitational-wave inference in Python.
dingo-gw / DingoDingo: Deep inference for gravitational-wave observations
portsmouth / GravyA WebGL simulation of gravitational lensing
harrism / Mini NbodyA simple gravitational N-body simulation in less than 100 lines of C code, with CUDA optimizations.
SajadAHMAD1 / Chaotic GSA For Engineering Design ProblemsAll nature-inspired algorithms involve two processes namely exploration and exploitation. For getting optimal performance, there should be a proper balance between these processes. Further, the majority of the optimization algorithms suffer from local minima entrapment problem and slow convergence speed. To alleviate these problems, researchers are now using chaotic maps. The Chaotic Gravitational Search Algorithm (CGSA) is a physics-based heuristic algorithm inspired by Newton's gravity principle and laws of motion. It uses 10 chaotic maps for global search and fast convergence speed. Basically, in GSA gravitational constant (G) is utilized for adaptive learning of the agents. For increasing the learning speed of the agents, chaotic maps are added to gravitational constant. The practical applicability of CGSA has been accessed through by applying it to nine Mechanical and Civil engineering design problems which include Welded Beam Design (WBD), Compression Spring Design (CSD), Pressure Vessel Design (PVD), Speed Reducer Design (SRD), Gear Train Design (GTD), Three Bar Truss (TBT), Stepped Cantilever Beam design (SCBD), Multiple Disc Clutch Brake Design (MDCBD), and Hydrodynamic Thrust Bearing Design (HTBD). The CGSA has been compared with seven state of the art stochastic algorithms particularly Constriction Coefficient based Particle Swarm Optimization and Gravitational Search Algorithm (CPSOGSA), Standard Gravitational Search Algorithm (GSA), Classical Particle Swarm Optimization (PSO), Biogeography Based Optimization (BBO), Continuous Genetic Algorithm (GA), Differential Evolution (DE), and Ant Colony Optimization (ACO). The experimental results indicate that CGSA shows efficient performance as compared to other seven participating algorithms.
deadskull7 / Human Activity Recognition With Neural Network Using Gyroscopic And Accelerometer VariablesThe VALIDATION ACCURACY is BEST on KAGGLE. Artificial Neural Network with a validation accuracy of 97.98 % and a precision of 95% was achieved from the data to learn (as a cellphone attached on the waist) to recognise the type of activity that the user is doing. The dataset's description goes like this: The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window). The sensor acceleration signal, which has gravitational and body motion components, was separated using a Butterworth low-pass filter into body acceleration and gravity. The gravitational force is assumed to have only low frequency components, therefore a filter with 0.3 Hz cutoff frequency was used.
kazewong / JimGravitational-wave data analysis tools in Jax
nanograv / EnterpriseENTERPRISE (Enhanced Numerical Toolbox Enabling a Robust PulsaR Inference SuitE) is a pulsar timing analysis code, aimed at noise analysis, gravitational-wave searches, and timing model analysis.
