In the afternoon of February 20, 2018, MATHMOD Conference offers different Tutorials, which can be attended by Ccnference participants as well as interested persons. To register for a tutorial, please utilize the link https://cocos.tuwien.ac.at/mathmod/.
- T1: Hands-on Workshop: Modeling Physical Systems with MATLAB®, Simulink®, and Simscape™
- T2.1: Interactive Content for Education with Maple T.A. and Möbius
- T2.2: Physical Modelling with MapleSim
- T3.1: Introduction to Physical Modeling with the Open Standards Modelica and FMI
- T3.2: Introduction to Modeling with Dymola and Modelica Libraries
- T4: Scalable Data Analytics with MATLAB
- T5.1: Netlogo Primer
- T5.2: Using AnyLogic for Multi-Method Simulation
- T6.1: Simulation Pipelines for Evaluation of Health Technologies
- T6.2: How to Choose Modelling Methods and Simulation Tools for Complex Dynamic Systems
- WS: Workshop on MVStudium/RMD
Modeling multiphysical systems is most effectively done using acausal, object-oriented methods. Simscape enables you to use these methods while leveraging MATLAB capabilities within the Simulink environment. In this hands-on workshop, will you assemble models of physical systems on your computer and run simulations to explore designs using optimization algorithms. The exercises include electrical, mechanical, and fluid systems, and you can create your own components by defining equations in a text based language.
- Introduction to Simscape Products
- Modeling a Mass-Spring-Damper
- Modeling a DC Motor
- Estimating Motor Parameters with Measured Data
- Modeling 3-D Mechanical Systems
- Connecting Electrical and 3-D Mechanical Sys.
- Modeling a Hydraulic Actuation System
- Modeling Custom Mechanical Springs
Maple T.A. and Möbius are Maple based platforms to provide practice as well as materials to support lectures or even shift lectures into distance learning. This interface not only administers content but also provide a randomized pool of examples and the possibility to present content in an interactive way. Not only mathematical courses but also courses dealing with physical, mechanical or numerical aspects can be implemented via Mathapps and Maple T.A. questions.
Prior knowledge of Maple is not required but beneficial.
Maplesoft has recently introduced version 2017 of MapleSim, the high-performance modelling and simulation software that combines an intuitive user interface with advanced techniques for the automatic derivation of system equations and their numerical solution. Based on the renowned Maple software as its computational core, it allows mixed acausal (topological) and causal (signal flow oriented) modelling of technical systems arising from physical domains such as electrics and electronics, 1D mechanics (rotational and translational), 3D mechanics (multibody systems), thermodynamics, signal flow (including controllers), hydraulics and magnetics. MapleSim comes with hundreds of components for these domains, mostly implemented in Modelica as a standard language.
Other features cover post-processing (equation extraction and manipulation, report generation, detailed analysis, etc.) through built-in templates, which are customizable Maple worksheets.
In this tutorial, we give a brief introduction to basic aspects of MapleSim, including:
- Using the GUI (entering diagrams, creating subsystems, supplying parameters, running simulations, manipulating plots)
- Post-processing (extraction of equations, analysis and optimization, C code generation and export to other environments, short overview of MapleSim API in Maple; new analysis templates and apps)
- Various examples (built-in and others)
- Technical topics (DAE solvers, platforms, etc.)
- New extensions like full FMI Connectivity (import / export), CAD Toolbox, add-on libraries
We also give an outlook on future developments for 2018. Feature suggestions are welcome!
Prior knowledge of Maple or MapleSim is not required.
This tutorial offers a introduction to Modelica, a non-proprietary, object-oriented, equation based language for modeling complex cyber-physical systems. An overview about the eco-system consisting of Modelica Simulation Tools, Modelica Standard Library, industrial use cases and associated standards like the Functional Mock-up Interface (FMI) is presented.
This tutorial gives an introduction to the Modelica-based simulation tool Dymola. Models are built textually or graphically, based on existing model libraries as well as own equations. Simulation, post-processing, scripting and external interfaces are explained. An overview of available model components from free and commercial libraries is presented.
Learn how the latest Software Tools for Data Analytics, Machine Learning and Big Data, can bring value to you. MathWorks invites you to this unique Workshop where subject matter expert will present and share experience with hands-on examples.
Companies that make industrial equipment are storing large amounts of machine data, with the notion that they will be able to extract value from it in the future. However, using this data to build accurate and robust models for prediction requires a rare combination of equipment, expertise, and statistical know-how.
In this workshop, we will use machine learning techniques in MATLAB® to estimate the remaining useful life of equipment. Using data from a real-world example, the session explores how MATLAB is used to build prognostic algorithms and take them into production, enabling companies to improve the reliability of their equipment and build new predictive maintenance services.
The workshop will cover basics of the multi-agent simulation NetLogo (open source, available for windows, mac and linux, both as 2D and 3D version). Attendees will learn basics of working with agents and cells (which form the natural environment of the former) in a hands-on manner. Please be sure to bring your laptop and download/install NetLogo from https://ccl.northwestern.edu/netlogo/download.shtml. No prior programming knowledge is necessary, we will learn everything we need during the course.
Speaker: M.Bicher, COCOS TU Wien and B. Glock, dwh GmbH
Date: Feb. 20, 2018, 3:30-6:00 pm
AnyLogic is currently one of the few simulation tools that is explicitly designed to develop and simulate models that are designed by coupling different modelling concepts. Via a block-oriented interface a modeller may combine the modelling methods: system dynamics, discrete event-, and agent-based- simulation to develop highly performant multi-method models. Participants of this tutorial expect hands-on introductions to the basics of the software and to different model-coupling strategies in AnyLogic. Hence, it is recommended to bring an own notebook.
Date: Feb. 20, 2018, 1:00-3:00 pm
Supporting health system decisions needs innovative analysis and modelling approaches as well as linking and processing of high sensitive data. Based on experiences of DEXHELPP, which supports national and international researchers, hands on solutions for research pipelines for evaluating health technologies will be presented, pitfalls will be described, and actual developments will be discussed.
Date: Feb. 20, 2018, 3:30-6:00 pm
Interdisciplinary research projects often focus on complex dynamic systems which consist of different subsystems in different domains. Based on the ARGESIM/SNE benchmarks this workshop will present an overview on established modelling methods and will discuss the process how to choose the right tools as well as possibilities for coupling of methods.
Speaker: Y. Senichenkov, St. Petersburg State Polytechnic University and A. Urquia, National Distance Education University (UNED) Madrid
Date: Feb. 22, 2018, 5:00-7:00 pm
Rand Model Designer (RMD), which is a newer version of academic product MvStudium (MVS, Model Vision Studium), is a simulation modeling tool that allows the user to create and experiment with models of complex dynamic systems. RMD is a high-performance environment for the development of component models of complex dynamical systems. RMD uses an intuitive, object-oriented high-level modeling language, based on the object paradigm of UML, allowing quick and efficient creation of complex models. RMD allows to develop continuous, discrete and hybrid (continuous-discrete) models and conduct the interactive computational experiments with them.