{"id":106,"date":"2020-01-23T15:46:03","date_gmt":"2020-01-23T15:46:03","guid":{"rendered":"http:\/\/128.131.86.19\/?page_id=106"},"modified":"2025-01-17T15:15:51","modified_gmt":"2025-01-17T15:15:51","slug":"tutorials","status":"publish","type":"page","link":"https:\/\/www.mathmod.at\/index.php\/program\/tutorials\/","title":{"rendered":"MATHMOD Tutorials"},"content":{"rendered":"\n<p>In the afternoon of 18 February 2025, a number of tutorials  will be jointly organized by <em>MATHMOD 202<\/em>5 and Mathworks. The tutorials are assigned to one of two time slots (B1 13:30-15:30 and B2 16:00-18:00).<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Teaching with MATLAB<\/strong><em> <\/em>(B1)<\/li>\n\n\n\n<li><strong>MATLAB Biomedical Signals &#8211; AI Hands-On Workshop<\/strong> (B2)<\/li>\n\n\n\n<li><strong>MATLAB DEVS &#8211;&nbsp;Discrete Event Simulation<\/strong>&nbsp;(B1)<\/li>\n\n\n\n<li><strong>Modelica Thermofluid Stream Library<\/strong> (B1)<\/li>\n\n\n\n<li><strong>Modelica System Dynamics Library<\/strong>&nbsp;(B2)<\/li>\n\n\n\n<li><strong>Agent-based Modelling&nbsp;for Health Science<\/strong> (B1)<\/li>\n\n\n\n<li><strong>Reinforcement Learning with Applications<\/strong>&nbsp;(B2)<\/li>\n<\/ul>\n\n\n\n<p>All tutorials are open to MATHMOD 2022 participants, ASIM 2022 participants, and TU Wien members. However, a <a rel=\"noreferrer noopener\" href=\"https:\/\/www.asim-gi.org\/asim2022\/tutorials\/registration-tutorials\" target=\"_blank\">separate registration<\/a> (independent from conference registration) is necessary.<\/p>\n\n\n\n<p>Tutorials will be (partly) recorded in order to allow participation at parallel tutorials. Recorded tutorials and tutorial handouts  are available to registered participants also after the tutorial for at least a month.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Tutorial details<\/h3>\n\n\n\n<hr class=\"wp-block-separator has-css-opacity\"\/>\n\n\n\n<p><strong>Teaching with MATLAB<\/strong><\/p>\n\n\n\n<p>Create engaging, scalable instructions with MathWorks online learning tools.&nbsp;<br>Highlights include&nbsp;Creating Interactive Scripts and App, Moving to the Cloud,&nbsp;Sharing Content with course collaborators and students,&nbsp;Helping Students learn MATLAB, create and automatically grade MATLAB coding assignments with MATLAB Grader \u2013 incl. an applied example from TU Wien.<\/p>\n\n\n\n<p><em>Kathi Kugler, The MathWorks; Clara Horvath, Iris Feldhammer,&nbsp;Andreas K\u00f6rner, TU Wien<\/em>&nbsp;<\/p>\n\n\n\n<hr class=\"wp-block-separator has-css-opacity\"\/>\n\n\n\n<p><strong>MATLAB Biomedical Signals<\/strong><\/p>\n\n\n\n<p>Artificial Intelligence\u2019s (AI) primary aim in a health-related environment is to provide clinical decision and diagnostic support by analyzing relationships between treatment options and patient outcomes. AI has also been developed for patient monitoring and care, drug development and disease prevention.&nbsp; Medical device manufacturers are using these technologies to innovate their products to better assist health care providers and improve patient care.<br>In this workshop, you will learn how to develop AI applications using MATLAB on the vast data generated during the delivery of health care every day. You will find out about tools and fundamental approaches for developing advanced predictive models on biomedical signals. We will cover the entire AI pipeline from all the way from signal exploration to deployment using both machine learning and deep learning approaches. In this hands-on workshop, you will write code and use MATLAB Online to: 1. Annotate time series biomedical signals automatically, 2.Train AI models on GPUs in the cloud, 3. Create deep learning models using CNNs and LSTMs for biomedical signal data, 4.&nbsp;Create machine learning models for biomedical signal data, 5.&nbsp;Apply advanced signal pre-processing techniques for automated feature extraction, 6.&nbsp;Automatically generate code for edge deployment of AI models.<br>No installation of MATLAB is necessary. Please bring your laptop to the session.<\/p>\n\n\n\n<p><em>Kathi Kugler, The MathWorks<\/em>&nbsp;<\/p>\n\n\n\n<hr class=\"wp-block-separator has-css-opacity\"\/>\n\n\n\n<p><strong>MATLAB DEVS &#8211;&nbsp;Discrete Event Simulation<\/strong><\/p>\n\n\n\n<p>MATLAB: MATLAB DEVS &#8211; Discrete Event and Hybrid Simulation<br>If you are interested in finding out how a well defined formalism for discrete event systems is made accessible for engineers and is extended by continuous systems integration, this tutorial is for you.&nbsp;As we use M&amp;S as engineers, the underlying methodologies and algorithms often remain unexplained and vague.<br>The tutorial introduces the hyPDEVS Toolbox for MATLAB (former MatlabDEVS Tbx) which is based on the Parallel DEVS (PDEVS) extension of the Discrete EVent System Specification (DEVS) formalism and its associated abstract simulator algorithms.&nbsp;After providing a general understanding of how DEVS algorithms and DEVS modeling works, participants are guided through the process of modeling and simulation of a hybrid system.<br>Ideally, bring your laptop with MATLAB installed to the session, but joining without is possible, too. The TBX can be loaded from <a href=\"https:\/\/github.com\/cea-wismar\/hyPDEVS_Matlab\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/github.com\/cea-wismar\/hyPDEVS_Matlab<\/a>.<\/p>\n\n\n\n<p><em>Christina Deatcu, Univ. Wismar<\/em><\/p>\n\n\n\n<hr class=\"wp-block-separator has-css-opacity\"\/>\n\n\n\n<p><strong>Modelica&nbsp; Thermofluid Stream Library<\/strong><\/p>\n\n\n\n<p>Are you interested in the efficient simulation of thermal architectures such as battery cooling for electric cars or reversible heat-pumps for building physics? Then this tutorial is for you. It provides an introduction into the DLR Thermofluid Stream Library, a free and open-source Modelica package:&nbsp;<a href=\"https:\/\/github.com\/DLR-SR\/ThermofluidStream\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/github.com\/DLR-SR\/ThermofluidStream<\/a><br>We explain the underlying methodology that enables the unique robustness of this approach, we present simple examples to follow by yourself, demonstrate the scalability of the approach to complex applications and perform a small hands-on optimization exercise.&nbsp;You can follow the tutorial without equipment but having a laptop with OpenModelica or Dymola installed will enable you to take more out of it.<\/p>\n\n\n\n<p><em>Dirk Zimmer, DLR<\/em>&nbsp;<\/p>\n\n\n\n<hr class=\"wp-block-separator has-css-opacity\"\/>\n\n\n\n<p><strong>Modelica System Dynamics Library<\/strong><\/p>\n\n\n\n<p>System Dynamics (SD) is a modeling technique that is mostly used in&nbsp;non-technical applications like economy or ecology. Several commercial&nbsp;simulation environments are available, but using an open source library, it can&nbsp;be used in Modelica and combined with other modeling paradigms.<br>The tutorial presents some basic SD applications and shows, how to implement&nbsp;them in a Modelica tool. It addresses SD experts, who want to use SD in&nbsp;Modelica, Modelica enthusiasts, who are interested in another modeling&nbsp;technique, and complete newbies. If time allows, we could also have a quick&nbsp;look into the internals of the open-source library.<\/p>\n\n\n\n<p><em>Peter Junglas, PHWT Vechta<\/em><\/p>\n\n\n\n<hr class=\"wp-block-separator has-css-opacity\"\/>\n\n\n\n<p><strong>Agent-based Modelling&nbsp;for Health Sciences<\/strong><\/p>\n\n\n\n<p>Agent based applications are simulation techniques for real-world problems with eponymous agents as decision-making entities. Each of them evaluates its situation and chooses the next action according to a set of rules. Communication between agents leads to complex behavior patterns and provides valuable information about the dynamics of the real-world problems it emulates.<br>This tutorial provides a more detailed look at data processes in healthcare systems on the one hand, and COVID-19 scenario calculation using agent-based models on the other.<\/p>\n\n\n\n<p><em>Dominik Rothschedl, Jakob Rosenberger, dwh Wien &amp; TU Wien<\/em><\/p>\n\n\n\n<hr class=\"wp-block-separator has-css-opacity\"\/>\n\n\n\n<p><strong>Reinforcement Learning with Applications<\/strong><\/p>\n\n\n\n<p>Reinforcement Learning (RL), as a branch of artificial intelligence,&nbsp;provides a framework to imitate a natural, evolutionary learning&nbsp;process of agents in complex environments. It can lead to complex&nbsp;behavior patterns (policies) which are difficult to describe in a rule-based way. Significant advances have been made in applying deep learning methods to certain RL algorithms to reduce the computational cost of these methods.<br>This tutorial gives an introduction of basic algorithms (Q-Learning,&nbsp;Dyna) and leads to more complex applications in an agent-based&nbsp;simulation context, such as predator-prey models and finding policies&nbsp;in pathfinding.<\/p>\n\n\n\n<p><em>Matthias Wastian, Dominik Brunmeir, dwh Wien &amp; TU Wien<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In the afternoon of 18 February 2025, a number of tutorials will be jointly organized by MATHMOD 2025 and Mathworks. The tutorials are assigned to one of two time slots (B1 13:30-15:30 and B2 16:00-18:00). All tutorials are open to MATHMOD 2022 participants, ASIM 2022 participants, and TU Wien members. However, a separate registration (independent &hellip; <a href=\"https:\/\/www.mathmod.at\/index.php\/program\/tutorials\/\" class=\"more-link\">Continue reading <span class=\"screen-reader-text\">MATHMOD Tutorials<\/span> <span class=\"meta-nav\">&rarr;<\/span><\/a><\/p>\n","protected":false},"author":3,"featured_media":0,"parent":31,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"ngg_post_thumbnail":0,"footnotes":""},"class_list":["post-106","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.mathmod.at\/index.php\/wp-json\/wp\/v2\/pages\/106","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.mathmod.at\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.mathmod.at\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.mathmod.at\/index.php\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/www.mathmod.at\/index.php\/wp-json\/wp\/v2\/comments?post=106"}],"version-history":[{"count":7,"href":"https:\/\/www.mathmod.at\/index.php\/wp-json\/wp\/v2\/pages\/106\/revisions"}],"predecessor-version":[{"id":1346,"href":"https:\/\/www.mathmod.at\/index.php\/wp-json\/wp\/v2\/pages\/106\/revisions\/1346"}],"up":[{"embeddable":true,"href":"https:\/\/www.mathmod.at\/index.php\/wp-json\/wp\/v2\/pages\/31"}],"wp:attachment":[{"href":"https:\/\/www.mathmod.at\/index.php\/wp-json\/wp\/v2\/media?parent=106"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}