4 repositorios
Awesome GitHub RepositoriesDynamic System Simulators
Tools for generating synthetic sensor data and trajectories to validate tracking and estimation algorithms.
Distinct from Scientific Computing and Simulation: Focuses on synthetic data generation for algorithm validation, distinct from general scientific computing platforms.
Explore 4 awesome GitHub repositories matching scientific & mathematical computing · Dynamic System Simulators. Refine with filters or upvote what's useful.
Awesome Dynamic System Simulators GitHub Repositories
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- rlabbe/kalman-and-bayesian-filters-in-python
rlabbe/Kalman-and-Bayesian-Filters-in-Python
19,050Ver en GitHubEste proyecto es un recurso educativo y un kit de herramientas para implementar estimación bayesiana y filtros de Kalman en Python. Proporciona un marco para construir filtros lineales y no lineales con el fin de estimar el estado de sistemas dinámicos combinando datos de sensores ruidosos con modelos matemáticos de procesos. La biblioteca se centra en la estimación probabilística de estados, utilizando actualización bayesiana recursiva y modelado matemático de espacio de estados para refinar las creencias sobre los estados del sistema. Incluye utilidades para simular sistemas dinámicos, lo que permite a los usuarios generar trayectorias sintéticas y observaciones de sensores para validar algoritmos de seguimiento frente a datos de referencia conocidos. La colección cubre conceptos fundamentales en ingeniería de sistemas de control, navegación robótica y fusión de datos de sensores. Está estructurada como una guía completa que combina explicaciones teóricas con implementaciones prácticas de código para calcular distribuciones de probabilidad y gestionar la incertidumbre en entornos dinámicos.
Includes utilities for simulating dynamic systems to generate synthetic data for algorithm validation.
Jupyter Notebook - scipy/scipy
scipy/scipy
14,474Ver en GitHubSciPy is a scientific computing library for Python that provides a comprehensive collection of mathematical algorithms and numerical tools for research and engineering. It functions as a high-performance numerical analysis framework, bridging high-level Python code with compiled C and Fortran routines to execute complex computations at hardware speeds. The library is built upon array-based data structures that utilize strided memory layouts to enable efficient data manipulation and slicing. By employing vectorized operation dispatch and linking to optimized hardware-specific linear algebra li
Models and predicts the behavior of physical processes over time using numerical solutions for differential equations.
Pythonalgorithmsclosemberpython - robotlocomotion/drake
RobotLocomotion/drake
3,910Ver en GitHubDrake is a robotics simulation framework and control system modeling tool used for designing, simulating, and verifying the dynamics of complex robotic systems. It functions as a multibody dynamics simulator and a mathematical optimization library, providing a suite of algorithms for trajectory optimization and the simulation of articulated robots. The framework is distinguished by its block-diagram system for composing dynamical subsystems and its ability to formulate and solve diverse mathematical programs, including linear, quadratic, and nonconvex nonlinear problems. It supports specializ
Executes numerical integration of dynamical systems over time using variable-step solvers and event detection.
C++drakerobotics - rlabbe/filterpy
rlabbe/filterpy
3,772Ver en GitHubfilterpy is a toolkit for Bayesian state estimation, Gaussian statistical analysis, and time-series noise reduction. It provides a library of linear and non-linear Kalman filters, as well as routines for non-Gaussian state estimation and signal smoothing. The project implements a variety of estimation methods, including particle filtering using Markov Chain Monte Carlo and resampling, and discrete Bayes filtering. It also includes a suite of algorithms for refining historical state estimates through backward and fixed-lag smoothing. Additional capabilities cover multivariate Gaussian analysi
Generates noise matrices and simulates uncertainty within physical systems for algorithm validation.
Python
