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ai-economist - Foundation is a flexible, modular, and composable framework to model socio-economic behaviors and dynamics with both agents and governments
This repo contains an implementation of Foundation, a framework for flexible, modular, and composable environments that model socio-economic behaviors and dynamics in a society with both agents and governments. The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies, Stephan Zheng, Alexander Trott, Sunil Srinivasa, Nikhil Naik, Melvin Gruesbeck, David C. Parkes, Richard Socher.
ai ml deep-reinforcement-learning economics multi-agent simulation-framework python-3 multi-agent-reinforcement-learning reinforcement-learning-environmentsPYLEECAN - Electrical engineering open-source software providing a user-friendly, unified, flexible simulation framework for the multiphysic design and optimization of electrical machines and drives
PYLEECAN project provides a user-friendly, unified, flexible simulation framework for the multiphysic design and optimization of electrical machines and drives. The main objective of PYLEECAN is to boost reproducible research and open-science in electrical engineering. Thus, it is intended for researchers, R&D engineers and teachers in electrical engineering, both on standard and novel topologies of electrical machines.
engineering electrical-engineering simulation-framework cad cam autocad-alternative simulation multiphysic non-linear electric-motorbrian2 - Brian is a free, open source simulator for spiking neural networks.
Brian is a free, open source simulator for spiking neural networks. It is written in the Python programming language and is available on almost all platforms. We believe that a simulator should not only save the time of processors, but also the time of scientists. Brian is therefore designed to be easy to learn and use, highly flexible and easily extensible. Brian2 is released under the terms of the CeCILL 2.1 license.
neuroscience science differential-equations spiking-neural-networks biological-simulations code-generation simulation-framework briancloudsim-plus - CloudSim Plus: A modern, full-featured, highly extensible and easier-to-use Java 8 Framework for Modeling and Simulation of Cloud Computing Infrastructures and Services
CloudSim Plus: A modern, highly extensible and easier-to-use Java 8 Framework for Modeling and Simulation of Cloud Computing Infrastructures and Services
simulation-framework cloud-computing cloud-simulation research cloud-infrastructure simulation java8 test-bed iaas paas saasSimDesign - Structure for organizing Monte Carlo simulations in R
To install the Github version of the package with devtools, type the following (assuming you have already installed the devtools package from CRAN). Go through the worked example to understand what the function inputs require, and try your luck walking through a debugging process though the edit argument (also very handy for writing the initial functions if you are comfortable with the debugging environment).
r simulation simulation-framework monte-carlo-simulationCoreNeuron - Simulator optimized for large scale neural network simulations.
CoreNEURON is a simplified engine for the NEURON simulator optimised for both memory usage and computational speed. Its goal is to simulate massive cell networks with minimal memory footprint and optimal performance. If you are a new user and would like to use CoreNEURON, this tutorial will be a good starting point to understand complete workflow of using CoreNEURON with NEURON.
neuron neural-network neuroscience compartmental simulation-framework supercomputing hpcseeds - Stochastic Cellular Artificial Life Simulator
The primary source for documentation is the SEEDS Wiki. Here, detailed installation instructions, how-to guides, code templates, and example experiments are provided. SEEDS requires Python version 2.7 or greater. As of version 1.0.9, SEEDS also supports Python 3. Additionally, SEEDS requires the NetworkX package.
evolution simulation-framework artificial-life cellular-automatagama - Core plug-in projects of the GAMA platform
Core plug-in projects of the GAMA platform
gama-platform gama simulation-framework simulation-environment simulation-toolkit modeling-language models agent-based-modeling mathematical-modelling hybrid-modeling gis spatial-models complex-systems simulation modeling modeling-agentsauryn - Auryn: A fast simulator for spiking neural networks with synaptic plasticity
Auryn is Simulator for recurrent spiking neural networks with synaptic plasticity. It comes with the GPLv3 (please see COPYING). will run the Vogels Abbott benchmark, a balanced network model with conductance based synapses. Spiking activity is written to files with the extension 'ras'.
simulation-framework auryn computational-neuroscience spiking-neural-networks c-plus-plus parallel-computingpowsimR - Power analysis is essential to optimize the design of RNA-seq experiments and to assess and compare the power to detect differentially expressed genes
For the installation, the R package devtools is needed. After installing the dependencies, powsimR can be installed by using devtools as well.
gene-expression single-cell rna-seq simulation-framework simulation power experimental-design power-analysis rrSFSW2 - rSFSW2: A R package to create soil water balance simulation experiment
Please cite the package if you publish results based on simulations carried out with our package, see citation("rSFSW2"), and we would like to hear about your publication. Download the package zip file via your web browser.
r simulation-framework r-package soil-water-balanceHELICS - Hierarchical Engine for Large-scale Infrastructure Co-Simulation (HELICS)
Welcome to the repository for the Hierarchical Engine for Large-scale Infrastructure Co-Simulation (HELICS). HELICS provides an open-source, general-purpose, modular, highly-scalable co-simulation framework that runs cross-platform (Linux, Windows, and Mac OS X). It is not a modeling tool by itself, but rather an integration tool that enables multiple existing simulation tools (and/or multiple instances of the same tool), known as "federates," to exchange data during runtime and stay synchronized in time such that together they act as one large simulation, or "federation". This enables bringing together established (or new/emerging) off-the-shelf tools from multiple domains to form a complex software-simulation without having to change the individual tools (known as "black-box" modeling). All that is required is for someone to write a thin interface layer for each tool that interfaces with existing simulation time control and data value updating, such as through an existing scripting interface. Moreover, the HELICS community has a growing ecosystem of established interfaces for popular tools, such that many users can simply mix and match existing tools with their own data and run complex co-simulations with minimal coding. Today the core uses of HELICS are in the energy domain, where there is extensive and growing support for a wide-range of electric power system, natural gas, communications and control-schemes, transportation, buildings, and related domain tools. However, it is possible to use HELICS for co-simulation in any domain. Previous and existing HELICS efforts have stretched across a wide range of scales in time and space: From transient dynamics (e.g. power system frequency response or electromechanical transient simulation) through steady-state power flow and markets to long-term planning studies. And from individual appliance behaviors, through distribution & bulk systems, and to nation-wide simulations.
simulation simulation-framework power-grids co-simulationsimobility - simobility - light-weight mobility simulation framework. Best for quick prototyping
simobility is a human-friendly Python framework that helps scientists and engineers to prototype and compare fleet optimization algorithms (autonomous and human-driven vehicles). It provides a set of building blocks that can be used to design different simulation scenarious, run simulations and calculate metrics. It is easy to plug in custom demand models, customer behavior models, fleet types, spatio-temporal models (for example, use OSRM for routing vehicles and machine learning models trained on historical data to predict ETA). Create an environment for experiments with machine learning algorithms for decision-making problems in mobility services and compare them to classical solutions.
simulator transportation ridesharing simulation-framework autonomous-vehicles mobility fleet-management optimization-algorithms mobility-modeling ridehailingxarray-simlab - Xarray extension and framework for computer model simulations
xarray-simlab is a Python library that provides both a generic framework for building computational models in a modular fashion and a xarray extension for setting and running simulations using the xarray's Dataset structure. It is designed for fast, interactive and exploratory modeling. xarray-simlab is well integrated with other libraries of the PyData ecosystem such as dask and zarr.
xarray simulation-framework modelling
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