Giter VIP home page Giter VIP logo

credibility's Introduction

Credibility

The BSD License GitHub release (latest SemVer)

Package Credibility is a free Modelica package to add traceability, uncertainty and calibration information to scalar and 1D-table parameters in a standardized way. For details of this library, see also the journal article Towards Modelica Models with Credibility Information.

Example for the kind of information that can be added to a scalar parameter value (image is from the article above; min/max/unit/description are standard Modelica attributes; the remaining information is new and is added via Modelica parameter records of the Credibility library):

SpringConstantCredibilityInfo

The Credibility library contains an example of a controlled drive train with scalar and 1D-table parameters with credibility information (Credibility.Examples.SimpleControlledDriveNonlinear). The models of this example are then used to demonstrate (for details see the article above):

  • optimization based calibration (using the commercial DLR Optimization Modelica library),
  • Monte Carlo simulation (demonstrating in particular a new approach for utilizing table-based uncertainty in a Monte Carlo simulation),
  • generation of an FMU (Functional Mock-up Unit) where the credibility information of the Modelica model is included in the FMU (generate the FMU from Credibility.Examples.SimpleControlledDriveNonlinear.SimpleDrive_forFMU with any Modelica tool that supports FMU export). With the many available FMI tools, the credibility information can be utilized.

The current approach has the drawback that data needs to be partially manually copied to perform calibration and Monte Carlo simulation in the many available tools for these tasks. The goal is to start a discussion in the Modelica Association how to improve this.

Main Developers

Martin Otter, Matthias Reiner, Jakub Tobolar, DLR - Institute of System Dynamics and Control.

Acknowledgement

The development of the Library was organized within the European ITEA3 Call6 project UPSIM – Unleash Potentials in Simulation (number 19006).The work was partially funded by the German Federal Ministry of Education and Research (BMBF, grant numbers 01IS20072H and 01IS20072G).

The development of this library is based on work carried out together with Leo Gall and Matthias Schäfer (both LTX Simulation GmbH) in the UPSIM project.

credibility's People

Contributors

martinotter avatar tobolar avatar

Stargazers

 avatar  avatar  avatar  avatar

Watchers

 avatar  avatar  avatar  avatar  avatar

credibility's Issues

Distinguish cause of uncertainty

It is not clear for uncertainty whether uncertainty within one quantity repeating the same measurement for just one part or uncertainty between measurements of different parts of the same kind.

E.g. measure the length of a part several times vs. measure the length of several parts.

Suggested solution: add corresponding flag to uncertainty description.

Problem with Table1D

Hi,
I tried to run your example with OMEdit but it always fails at Table1D,
The error can be seen in the screenshot
Untitled

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.