Search Results for "stochastic"
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Showing 52 open source projects for "stochastic"
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DifferentialEquations.jl
Multi-language suite for high-performance solvers of equations
This is a suite for numerically solving differential equations written in Julia and available for use in Julia, Python, and R. The purpose of this package is to supply efficient Julia implementations of solvers for various differential equations. The well-optimized DifferentialEquations solvers benchmark as some of the fastest implementations, using classic algorithms and ones from recent research which routinely outperform the “standard” C/Fortran methods, and include algorithms optimized... -
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OrdinaryDiffEq.jl
High performance ordinary differential equation (ODE)
This is a suite for numerically solving differential equations written in Julia and available for use in Julia, Python, and R. The purpose of this package is to supply efficient Julia implementations of solvers for various differential equations. The well-optimized DifferentialEquations solvers benchmark as some of the fastest implementations, using classic algorithms and ones from recent research that routinely outperform the “standard” C/Fortran methods, and include algorithms optimized... -
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DeepXDE
A library for scientific machine learning & physics-informed learning
...Solving forward/inverse integro-differential equations (IDEs) [SIAM Rev.] fPINN: solving forward/inverse fractional PDEs (fPDEs) [SIAM J. Sci. Comput.] NN-arbitrary polynomial chaos (NN-aPC): solving forward/inverse stochastic PDEs (sPDEs) [J. Comput. Phys.] PINN with hard constraints (hPINN): solving inverse design/topology optimization [SIAM J. Sci. Comput.] Residual-based adaptive sampling [SIAM Rev., arXiv] Gradient-enhanced PINN (gPINN) [Comput. Methods Appl. Mech. Eng.] PINN with multi-scale Fourier features [Comput. Methods Appl. Mech. Eng.] -
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LINE Solver
Queueing Theory Algorithms
LINE is an open-source software package to analyze queueing models via analytical methods and simulation. The solver is available for Java/Kotlin, MATLAB, and Python. LINE features algorithms for the solution of open queueing systems (e.g., M/M/1, M/M/k, M/G/1, ...), open and closed queueing networks, and layered queueing networks. Additional details are available on the project website: http://line-solver.sf.net. -
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Java Modelling Tools is a suite of scientific tools for performance analysis and modelling using queueing theory and colored stochastic Petri nets. Models are solved either with analytical, asymptotic or simulation methods; workload characterization tools are also included in the suite. See the project website for more details: http://jmt.sf.net
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Generic data-assimilation toolbox written in java, with native (c and fortran) libraries for high performance computing. Provides tools to couple to your own model and a wide range of algorithms, ranging from parameter calibration to Kalman filters.
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LabRPS
Random phenomena generator
...Code and release files are primarily hosted on https://github.com/LabRPS/LabRPS and mirrored here LabRPS aims to be a tool for the numerical simulation of random phenomena such as stochastic wind velocity, seismic ground motion, sea surface ... etc. It can be in a wide range of uses around engineering, such as random vibration or vibration fatigue in mechanical engineering, buffeting analysis in bridge engineering.... LabRPS is mainly to assist reseachers in related fields to quickly implement new simulation methods programmatically in their new research work based on the existing works, help engineers to numerically generate random phenomena in a more realistic way, helps students and new comers to this field to learn quickly. ... -
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Data Envelopment Analysis using Stata
Unified Data Envelopment Analysis commands for Stata
DEAS provides Data Envelopment Analysis tools for Stata. Version 2.0 is a major update centered on a unified dea command for Stata 16 or later. It consolidates sixteen DEA model families into one interface, keeps backward compatibility with the original inputs = outputs syntax, and adds a fast Mata two-phase revised simplex engine alongside Stata's LinearProgram() IPM engine. The package supports radial technical efficiency (CCR/BCC), SBM, additive model, radial and SBM super-efficiency,... -
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...Zgoubi simulates beam dynamics and polarization in a variety of accelerators (storage ring, synchrotron, cyclotron, betatron, microtron, FFAG, multi-pass ERL, etc) and optical systems (beam lines, magnetic and electrostatic optical components, time-of-flight and mass spectrometers, etc). The code includes built-in fitting procedures with a wide variety of constraints; stochastic SR energy loss; the tracking of synchrotron radiation (SR) Poynting vector; space charge models; various Monte Carlo procedures, etc. Contact: francoisgmeot@gmail.com Documentation (History of accelerators that zgoubi deals with, theory, tutorials): https://link.springer.com/book/10.1007/978-3-031-59979-8 https://link.springer.com/book/10.1007/978-3-031-16715-7, Chap. 14.
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StochSD
CSS package for contin., discrete and combined modelling & simulation
...In particular, StochSD includes features for stochastic modelling and post analysis of multiple simulations followed by presentation of the results in statistical form. Tools for Sensitivity Analysis, Optimization and Statistical Analysis are parts of StochSD. -
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PCRaster
PCRaster environmental modeling software
...A Python framework supports Monte Carlo simulations and data assimilation (Ensemble Kalman Filter and Particle Filter). The Aguila tool allows for the interactive visualisation of stochastic spatio-temporal data. -
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MesoRD is a stochastic and deterministic simulator of coupled chemical reactions and diffusions in space.
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Bridge.jl
A statistical toolbox for diffusion processes
Statistics and stochastic calculus for Markov processes in continuous time, include univariate and multivariate stochastic processes such as stochastic differential equations or diffusions (SDE's) or Levy processes. -
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ABC-SysBio implements likelihood free parameter inference and model selection in dynamical systems. It is designed to work with both stochastic and deterministic models written in Systems Biology Markup Language (SBML).
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MED-FOES
An Agent-Based Simulation of Invasive Pest Insects
MED-FOES is an implementation of a stochastic Agent-Based Simulation (ABS) system for analyzing demographic changes in a single population of Medfly (Ceratitis capitata) over time. The simulation currently takes population extinction as a given because it aims to give us better estimates of necessary quarantine lengths in areas where Medfly is not currently established but does repeatedly invade. -
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Cambridge Rocketry Simulator
Simulate high power rocket flights with splash down plots
...Running in Monte Carlo mode allows generates multiple possible flight paths and splash down plots, indicating the probability of landing in an area. Peer-reviewed publication in the Journal of Open Research Software (JORS) http://doi.org/10.5334/jors.137 "Cambridge Rocketry Simulator – A Stochastic Six-Degrees-of-Freedom Rocket Flight Simulator" -
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StochPy - Stochastistic modeling in Python - is an easy-to-use package, which provides several stochastic simulation algorithms and unique analysis techniques, which can be used for stochastic simulations of biochemical systems.
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Virus QSP Modeling
C++ and Python code for simulating RNA virus replication
Stochastic simulation model of poliovirus Sabin-to-Mahoney genetic state transition (C++ code). Models genotypes, virus populations, and quasispecies cloud. Simulates replication error and copy-choice recombination. Various parameters guiding the model are user-specified. Python code post-processes simulation output to produce report files. -
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Lsie
General purpose Lindenmayer system written in C++
Lsie (Elsie) is a Lindenmayer system implementation written in C++. It supports bracketed, stochastic, context sensitive, parametric, conditional grammars with parameter signature matching, homomorphism and decomposition. Expression evaluation and signature matching is based on an internal bytecode compiler and interpreter, types are statically checked at compile time. It may seem a bit overkill, but it is fun to write and it has some practical advantages. :) The project is work in progress. ... -
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RSSA
Rejection-based stochastic simulation algorithm (RSSA)
Rejection-based stochastic simulation algorithm (RSSA) is an efficient exact algorithm for doing stochastic simulation of biochemical reaction systems. RSSA improves over state of the art of stochastic simulations by avoiding and collapsing as much the number of propensity updates. -
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fesslix
Stochastic Analysis
A brief summary of the main features of Fesslix: - Perform non-intrusive reliability analysis or Bayesian updating either --- by running commands on the command line or --- by means of an Octave interface or --- by means of a Python interface - Flexible input language for writing Fesslix parameter files --- Control flow statements (e.g. if, for, while) --- Most parameters can be defined as functions - Working with response surfaces - Linear finite element analysis using truss, beam and plane stress/strain elements - Spectral Stochastic Finite Elements - Bayesian networks This is the download page for Windows executables of Fesslix. -
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Molecular Measurer
Parallel, Stochastic Measurement of Molecular Surface Area
A program to measure molecular surface area. The algorithm is GPU-accelerated using CUDA and is described in the research paper linked from the project home page. The input to the program is xyzr files, which can be generated from pdb files. -
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abc-sde
approximate Bayesian computation for stochastic differential equations
A MATLAB toolbox for approximate Bayesian computation (ABC) in stochastic differential equation models. It performs approximate Bayesian computation for stochastic models having latent dynamics defined by stochastic differential equations (SDEs) and not limited to the "state-space" modelling framework. Both one- and multi-dimensional SDE systems are supported and partially observed systems are easily accommodated. -
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NAADSM
A framework for simulating the spread and control of animal diseases
NAADSM is a framework for creating stochastic, spatial, state-transition simulation models of the spread and control of highly contagious diseases of animals. User-established parameters define model behavior in terms of disease progression; disease spread by animal-to-animal contact, contact with contaminated personnel or equipment, and airborne dissemination; and the implementation of control measures such as vaccination and depopulation.