3 रिपॉजिटरी
Computation of system-wide metrics and temporal averaging of data within numerical simulations.
Distinct from System Performance Analyzers: Closest candidates focus on e-commerce quantities or system hardware performance, not physical simulation data analysis.
Explore 3 awesome GitHub repositories matching scientific & mathematical computing · Simulation Metrics Analysis. Refine with filters or upvote what's useful.
Mesa is a Python framework for agent-based modeling and complex systems simulation. It provides a toolkit for creating simulations where autonomous agents interact within a shared environment to observe emergent global behaviors. The project includes a browser-based interface for real-time visualization of simulation states and agent interactions. It also functions as a data analysis library for recording and processing metrics from model runs to quantify system behavior. The framework supports multiple environmental topologies, including grid-based spatial mapping and network-graph topologi
Collects and processes metrics from model runs to identify patterns using statistical analysis tools.
DifferentialEquations.jl is a comprehensive numerical library designed for solving ordinary, stochastic, delay, and algebraic differential equations. It functions as a high-performance solver suite that integrates scientific machine learning, probabilistic programming, and automated differentiation into a unified framework. By leveraging multiple dispatch and symbolic-numeric integration, the library provides a flexible environment for complex mathematical modeling and simulation. The project distinguishes itself through its ability to bridge traditional numerical analysis with modern machine
Computes mean, variance, and covariance bounds across ensemble trajectories to quantify uncertainty in numerical experiments.
This project is a parallel simulation engine and molecular dynamics simulator designed to model the physical movements of atoms and molecules. It functions as an interatomic potential framework for calculating forces between particles and a materials analysis tool for computing thermodynamic, structural, and transport properties of solids and fluids. The engine is distinguished by its high-performance computing capabilities, utilizing spatial-domain decomposition and message-passing interface communication to distribute workloads across processors. It supports multi-backend GPU acceleration v
Provides capabilities to calculate system-wide metrics and perform spatial or temporal averaging of atomic data.