{"id":987,"date":"2022-03-13T18:49:31","date_gmt":"2022-03-13T18:49:31","guid":{"rendered":"https:\/\/elo-x.eu\/?p=987"},"modified":"2023-01-18T10:58:50","modified_gmt":"2023-01-18T10:58:50","slug":"first-elo-x-seasonal-school-and-workshop","status":"publish","type":"post","link":"https:\/\/elo-x.eu\/?p=987","title":{"rendered":"First ELO-X Seasonal School and Workshop"},"content":{"rendered":"\t\t
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\n\t\t\t\t\t\t\t\t\n\t\t\t\tFirst ELO-X Seasonal School and Workshop \n\t\t\t<\/h2 > \n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tMarch 21-30, Leuven<\/a>, Belgium<\/span><\/span>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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The first on-site event organized by ELO-X took place in Leuven, Belgium from March 21 to 30, 2022. The event included a four-day seasonal school on training in technical and soft skills at KU Leuven<\/a>, followed by a two-day workshop on smart industrial control systems hosted by Siemens<\/a>. The detailed schedule is attached to this page.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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The First ELO-X Seasonal <\/span><\/span>School @ KU Leuven<\/span><\/span><\/p>

Organizers: Prof. Jan Swevers<\/a>, Prof. Panos Patrinos<\/a><\/span><\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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March 21, 2022<\/i><\/b><\/p>

Monday<\/i><\/b><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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18:30 – 22:00\u00a0<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\t\t\t\tWelcome Dinner @ Oude Kantien<\/span><\/a><\/span>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t
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March 22, 2022 <\/i><\/b><\/p>

Tuesday<\/i><\/b><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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@ Thermotechnisch Instituut<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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08:15 – 09:00<\/span><\/p>

09:00 – 10:30<\/span><\/p>

10:30 – 11:00<\/span>\u00a0<\/span><\/p>

11:00 – 13:00\u00a0<\/span>
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13:00 – 14:00\u00a0<\/span>
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14:00 – 15:00<\/span>
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15:00 – 15:30<\/span>
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15:30 – 16:30<\/span>
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Registration @ Machinezaal<\/span><\/a><\/span><\/p>

Presentation Skills for Researchers (Part I)<\/span><\/p>

Coffee Break<\/span><\/span><\/p>

Presentation Skills for Researchers (Part II)<\/span><\/p>

Sandwich Lunch<\/span>
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Model Predictive Control: from Basics to Learning Based Design (Part I) [Video]<\/span><\/a><\/span>
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Coffee Break<\/span>
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Model Predictive Control: from Basics to Learning Based Design (Part II)<\/span><\/a> [Video]<\/span><\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\t\t\t\tIda Tassens<\/span><\/a>
\n\nAnnelien De Geest<\/span><\/a> & <\/span>Colette Coppens<\/span><\/a>



\n\nProf. Alberto Bemporad<\/span><\/a>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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The first part is an interactive seminar that focuses on some key issues that many presenters struggle with, ranging from presentation structure to maintaining audience attention and preparing for the Q&A session. We will also provide language advice and practice useful English phrases (e.g. to structure your talk).<\/span><\/p>

In the second part, we will focus on the participants’ own presentations. Participants take turns presenting and receive individual feedback from both the tutor and the peer group. We will also use these presentations as a starting point to discuss some other aspects of presentations skills, such as body language and visuals. By the end of the session, participants are aware of their own strengths and weaknesses as a presenter and have been given new tools to enhance their academic presentation skills in English.<\/span><\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t

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Model Predictive Control (MPC) is a well-established technique for controlling multivariable systems subject to constraints on manipulated and output variables in an optimized way. It is currently adopted in many application areas, traditionally in the process industries and more recently also in the automotive and aerospace sectors, smart energy grids, financial engineering, and many others. In this lecture, I will provide an introduction to MPC, from its basic formulation for linear dynamical systems with constraints to its extension to linear time-varying and nonlinear systems, and present embedded optimization methods tailored to the implementation of MPC. I will also cover more recent approaches based on machine learning tools to facilitate and boost the design of MPC controllers, including methods to derive nonlinear control-oriented models (such as recurrent neural networks) from data and adapt them online, and to reduce controller calibration efforts by learning surrogate models of the closed-loop performance to optimize.<\/span><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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March 23, 2022 <\/i><\/b><\/p>

Wednesday<\/i><\/b><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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09:00 – 10:30<\/span><\/p>

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10:30 – 11:00<\/span>\u00a0<\/span><\/p>

11:00 – 13:00<\/span><\/span><\/p>

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13:00 – 14:00\u00a0<\/span>
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14:00 – 15:30<\/span>
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15:30 – 16:00<\/span>
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16:00 – 18:00<\/span>
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A Quick Guide towards Connecting Communication: Giving & Receiving Feedback and Listening to Intention & Message (Part I)
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Coffee Break<\/span>
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A Quick Guide towards Connecting Communication: Giving & Receiving Feedback and Listening to Intention & Message (Part II)<\/span><\/p>

Lunch<\/span>
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Presentations by the ESRs<\/span><\/p>

Coffee Break<\/span><\/p>

Presentations by the ESRs<\/span>
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Els Deboutte<\/span><\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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How can you use the diversity in communication styles and preferences in your team to your benefit to ensure this complementarity is both productive and pleasant? In this workshop we\u2019ll focus on 2 main areas as a \u2018quick guide\u2019 to communication:<\/span><\/p>

1. Focus on you as a person: how do you communicate? What are my preferences? What works for me? What are my interaction \u2018allergies\u2019? How do others perceive me and is that coherent with how I see me? What are my strengths and pitfalls or blind spots?<\/span><\/p>

2. Focus on interaction and collaboration: How do humans respond to behavior? What interactions go naturally smooth and when does it take more work? Why is it worth to put that work in (or not, your choice)? How to give and receive feedback?<\/span><\/p>

The workshop is interactive and switches between theory and practice.<\/span><\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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March 24, 2022 <\/i><\/b><\/p>

Thursday<\/i><\/b><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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09:00 – 10:30<\/span><\/p>

10:30 – 11:00<\/span>\u00a0<\/span><\/p>

11:00 – 13:00\u00a0<\/span>
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13:00 – 14:00\u00a0<\/span>
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14:00 – 15:30<\/span>
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15:30 – 16:00<\/span>
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16:00 – 18:00<\/span>
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\n\t\t\t\t\t\t\t\t\tResearch Management\/Managing Your PhD (Part I)<\/span>
\n\nCoffee Break<\/span>
\n\nResearch Management\/Managing Your PhD (Part <\/span>II<\/span>)<\/span>\n
\nLunch<\/span>
\n\nResearch Management\/Managing Your PhD (Part III)<\/span>
\n\nCoffee Break<\/span>
\n\nResearch Management\/Managing Your PhD (Part IV)<\/span>\n<\/span><\/span><\/span>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tJoeri Wielandts<\/span><\/a>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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By nature, research is uncertain and requires flexibility. At the same time, to be able to deliver results, it also requires planning. In the first part of this workshop we explore planning techniques from traditional project planning and translate them to the research context. This way the participants will be able to apply this to their own work. They will not only be able to create \/ optimize their own planning, they\u2019ll be able to communicate about it with their supervisor and stakeholders. This is extra relevant to discuss and anticipate bottle necks, uncertainties, risks, \u2026<\/span>\n\nTranslating any planning into productive daily activities isn\u2019t possible without good time & self-management. In the second part of the workshop we\u2019ll explore two key concepts which are crucial to reach productivity: setting priorities and finding focus. By the end of the session the participants will be able to strengthen their current habits using practical insights and hand-on tips. <\/span><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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March 25, 2022 <\/i><\/b><\/p>

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@ Huis Bethlehem, Aula Wolfspoort<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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09:00 – 10:45<\/span><\/p>

10:45 – 11:15<\/span>\u00a0<\/span><\/p>

11:15 – 13:00<\/span>
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13:00 – 14:00\u00a0<\/span>
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14:00 – 15:45<\/span>
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15:45 – 16:15<\/span>
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16:15 – 18:00<\/span>
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19:00 – 22:00<\/span>
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Embedded Learning and Optimization Methods for Nonlinear Systems<\/span><\/a> [Video]<\/span><\/a><\/p>

Coffee Break<\/span><\/p>

Tutorial on the Behavioral Approach to Data-driven System Theory and Control<\/span><\/a>\u00a0<\/p>

Lunch<\/span><\/p>

Learning from Data: Model Representation and Duality<\/span><\/a> [Video]<\/span><\/a><\/p>

Coffee Break<\/span><\/p>

Data-driven Decision-making in Dynamic Environments<\/span><\/a> [Video]<\/span><\/a><\/p>

Social Dinner @ <\/span>Faculty club<\/span><\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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Prof. Moritz Diehl<\/span><\/a>

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Prof. Ivan Markovsky<\/span><\/a><\/p>


Prof. Johan Suykens<\/span><\/a><\/p>

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Prof. Peyman Mohajerin Esfahani<\/span><\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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This minicourse introduces participants into numerical optimization methods for estimation and control, spanning from suitable and numerically favourable problem formulations to structure exploiting algorithms. The focus is on moving horizon estimation (MHE) and model predictive control (MPC) formulations and nonlinear programming (NLP) algorithms for direct optimal control parameterizations like direct multiple shooting. We show, first, how to algorithmically exploit convex structures like nonlinear least-squares or L1-Penalties via so called Generalized Gauss-Newton (GGN) and Sequential Convex Programming (SCP) methods, and second, how to exploit non-convex structures like sparsity-enhancing penalties or complementarity constraints in so called Extended Gauss-Newton (XGN) and Mathematical Programming with Complementarity Constraint (MPCC) methods. The course presents joint work with Katrin Baumg\u00e4rtner, Florian Messerer and Armin Nurkanovic.<\/span><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/i><\/span>\n\t\t\t\t\t\t\t\t<\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\tAbstract for Tutorial on the behavioral approach to data-driven system theory and control<\/a>\n\t\t\t\t\t<\/h5>\n\n\t\t\t\t\t
The lecture gives a self-contained derivation of data-driven methods developed in the behavioral setting and demonstrates their relevance for real-life applications. The methods reviewed combine ideas from subspace identification and machine learning. A key idea from subspace identification is that under a persistency of excitation condition, the image of a Hankel matrix constructed from the data is equal to the behavior of the system. This result allows construction of trajectories directly from data, which in turn allows solving data-driven simulation, smoothing, and control problems. The construction requires solution of a system of linear equations only. It assumes, however, that the data is obtained from a linear time-invariant system. For noisy data and nonlinear systems, sparsity promoting regularization is an effective heuristic.<\/span><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t
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Neural networks and deep learning have attracted much attention in recent years, serving as powerful parametrizations for nonlinear functions in a wide range of different applications. On the other hand solid foundations have been established with support vector machines and kernel-based approaches in learning theory and optimization. The main scope of the lecture is to outline a unifying setting and discuss several new synergies between neural networks, deep learning and kernel machines. Duality principles and different model representations play a key role at this point. This will be explained both for supervised and unsupervised learning problems, and further extended to generative models, multi-view learning, and dynamical systems modelling.<\/span><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t
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In this seminar, we start with a broad class of fault detection and estimation for large-scale nonlinear dynamical systems. We discuss similarities and differences between this problem and classical supervised learning. We also highlight the connection to the concept of behavioral sets in dynamical systems. We continue the discussion with ideas that create synergy between traditional model-based approaches and modern data-driven analytics in order to address the inherent complexity of the problem. In the second part, we shift our attention to the performance guarantees of our proposed solution. In this part, we study this topic in a general context of data-driven decision-making. A particular focus will be on the role of convexity in different aspects including computational, statistical, and real-time implementation.<\/span><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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March 26, 2022 <\/i><\/b><\/p>

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\n\t\t\t\t\t\t\t\t\tSocial Event<\/span>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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The First ELO-X Workshop @ Siemens<\/span><\/span><\/p>

Organizer: Dr. Son Tong<\/a><\/span><\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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March 28, 2022<\/i><\/b><\/p>

Monday<\/i><\/b><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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@\u00a0Siemens Digital Industries Software<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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09:00 – 10:15<\/span><\/p>

10:15 – 10:30<\/span><\/p>

10:30 – 13:00<\/span><\/p>

13:00 – 14:00<\/span>\u00a0<\/span><\/p>

14:00 – 15:45\u00a0<\/span>
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\u00a0<\/span><\/span><\/p>

15:45 – 16:00<\/span>
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16:00 – 17:00<\/span>
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\u00a0<\/span><\/span><\/span><\/span><\/p>

17:00 – 18:00<\/span>
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\u00a0<\/span><\/span><\/span><\/span><\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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Deep Learning in Practice<\/span><\/p>

Coffee Break<\/span><\/p>

Deep Learning in Practice (cont.)<\/span><\/p>

Lunch <\/span><\/p>

Scientific Machine Learning @ Simulation and Test Systems – Connecting the Real and the Digital World<\/span><\/p>

Coffee Break<\/span><\/p>

Reinforcement Learning in the Context of Generative Engineering: Controller Generation Supporting Automated Architecture Tradeoff<\/span><\/p>

Numerical Optimization and Machine Learning for Edge Case Detection in Autonomous <\/span>Vehicles<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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Prof. Tinne Tuytelaars<\/span><\/a>

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Dr. Dirk Hartmann<\/span><\/a> & <\/span>Dr. Daniel Berger<\/span><\/a>


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Dr. Jonathan Menu <\/span><\/a>

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Dr. Roberto d’Ippolito<\/span><\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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In this class, my aim is to give some ‘tips and tricks’ on how to apply neural networks in practice. We will cover basic topics such as network architectures, loss functions, activation functions, transfer learning, adversarial learning, etc. Throughout, we will use computer vision applications as examples (mostly image classification and object detection, but also image generation).<\/span><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t
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With its unique and complete portfolio of hardware and software Siemens has transformed to the global leader of Industrial Internet of Things (IIoT). The software portfolio offered by Siemens Digital Industries Software covers the complete product life cycle – from early design to productization and all the way to maintenance and recycling. We will demonstrate how Artificial Intelligence and Machine Learning has become an integral part to many of our solutions and the key enabler for our vision of the executable digital twin, including technical deep dives on selected examples.<\/span><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t
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With generative engineering, Siemens Digital Industries Software launched a new approach and tool to computationally support the early system architecting phase. Rather than relying on experience or implicit decision making, thousands or millions of alternative architecture solutions can now be formally evaluated and compared. Since the evaluation of performance criteria for these system alternatives typically relies on assessing their controlled behaviour (e.g., through behavioural simulation), new approaches for controller generation are essential for the success of generative engineering. This talk will introduce generative engineering for system architecture and will show how reinforcement learning is a good candidate for supporting automated architecture tradeoff. It will also highlight a number of research challenges towards making RL more efficient in the specific early design context.<\/span><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t
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Validating the safety and reliability of ADAS and automated vehicle functionalities requires billions of testing kilometers. Before putting these complex systems into practice, it is therefore important to simulate how they will perform under different scenarios and all possible circumstances. In this talk, we will discuss some of our approaches to explore the scenario space then generate edge case scenarios using optimization and AI algorithms. Demonstrations on an AEBS (Advanced Emergency Braking System) application will be presented using Siemens Simcenter tools.<\/span><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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March 29, 2022<\/i><\/b><\/p>

Tuesday<\/i><\/b><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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@\u00a0Siemens Digital Industries Software<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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09:00 – 10:00<\/span><\/p>

10:00 – 11:30<\/span>\u00a0<\/span><\/p>

11:30 – 11:45\u00a0<\/span>
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11:45 – 13:00\u00a0<\/span>
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13:00 – 14:00<\/span><\/span><\/span><\/span><\/p>

14:00 – 18:00<\/span><\/span><\/span><\/span><\/span><\/p>

19:00 – 22:00<\/span><\/span><\/span><\/span><\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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Supervisory Board Meeting<\/span><\/p>

Distributed Learning Control with Communication Efficiency<\/span><\/p>

Coffee Break<\/span><\/p>

Machine Learning for Efficient Vehicle R&D Testing<\/span><\/p>

Lunch<\/span><\/p>

Mid-term Check<\/span><\/p>

Farewell Dinner @ Mykene<\/span><\/a><\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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Prof. Konstantinos Gatsis<\/span><\/a>

\n\nDr. Bram Cornelis<\/span><\/a>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Recent years have seen cyber-physical system architectures becoming increasingly connected in local networks or connected to cloud infrastructure. In the main part of this seminar I will present how these connections permit learning and adapting to data collected in a distributed fashion. This problem is aligned with the emerging area of federated learning and I will discuss approaches to address the involved bandwidth and communication bottleneck. In the latter part of the seminar, I will discuss the converse problem of using data and learning techniques to design cyber-physical networks when models are not available.<\/span><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t
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Vehicles are increasingly getting more complex to design, because of the large number of design variants, the need for modular designs and the use of highly integrated systems due to e.g. the electrification trend. While Computer-Aided Engineering (CAE) tools are widely adopted, physical measurements remain an integral part of the methodology throughout all stages of the vehicle design process, for design verification and validation purposes but also for model updating in view of future design cycles. The industry is seeking solutions to increase the overall efficiency of physical testing, both in terms of equipment cost and in terms of time. Machine Learning (ML) methodologies are therefore envisioned to reduce the amount of time-intensive and repetitive manual labor and to achieve \u201cfirst time right\u201d testing (e.g. avoiding measurement mistakes). Also in the later stages of the vehicle lifecycle measurement data is collected, e.g. for the purpose of \u201cend-of-line\u201d production quality inspection. In this case ML can enable an automatic interpretation of the measured data in a quality inspection system.<\/span><\/p>

This presentation will discuss ongoing research activities at Siemens on the application of ML to vehicle testing through different use cases, e.g. data-driven virtual sensors for vehicle dynamics testing, sensor anomaly detection in large channel count measurement campaigns and deep learning based booming noise detection in acoustic end-of-line testing.
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March 30, 2022<\/i><\/b><\/p>

Wednesday<\/i><\/b><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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@\u00a0Siemens Digital Industries Software<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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09:00 – 12:00<\/span><\/p>

12:00 – 13:00<\/span> <\/span><\/p>

13:00 – 14:00<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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Mid-term Check<\/span><\/p>

Lunch<\/span><\/p>

Siemen\u2019s Workshop Tour<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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\nTuesday March 29, 2022<\/span>\n<\/th>\n<\/th>\n\n<\/tr>\n\n
\n14:00 – 15:45<\/span>\n<\/td>\n\nPO & PC presentations<\/span>\n<\/td>\n<\/tr>\n\n
\n15:45 – 16:15<\/span>\n<\/td>\n\nCoffee break<\/span>\n<\/td>\n<\/tr>\n
\n16:15 – 17:50<\/span>\n<\/td>\n\nFellows\u2019 individual presentations<\/span>\n<\/td>\n<\/tr>\n\n
\nWednesday March 30, 2022<\/span>\n<\/th>\n<\/th>\n\n
\n09:00 – 10:15<\/span>\n<\/td>\n\nFellows\u2019 individual presentations<\/span>\n<\/td>\n<\/tr>\n\n
\n10:15 – 10:45<\/span>\n<\/td>\n\nCoffee break<\/span>\n<\/td>\n<\/tr>\n\n
\n10:45 – 11:30<\/span>\n<\/td>\n\nRestricted session of PO with fellows<\/span>\n<\/td>\n<\/tr>\n\n
\n11:30 – 12:00<\/span>\n<\/td>\n\nAny other business\/individual meeting<\/span>\n<\/td>\n<\/tr>\n<\/tr>\n<\/tbody>\n<\/table><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Venue Information<\/span><\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Routes to the seasonal school (more details later…)<\/span><\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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How to reach <\/span>Department of Mechanical Engineering (MECH), KU Leuven<\/span><\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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Route to <\/span>Thermotechnisch Instituut<\/span><\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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From Hotel The Lodge Heverlee<\/span><\/strong><\/p>

It can be reached by walking<\/span><\/a>, which takes only 2 minutes. Then follow the signs or staff guidance to rooms.<\/span><\/p>

From Novotel Leuven Centrum<\/span><\/strong>
First, reach the Leuven Station by an 8-min walk. Then, take <\/span>Bus 616 (direction Heverlee)<\/span><\/a> from the Leuven Station bus stop number 6 (Leuven Station Perron 6), go down at the stop \u201cHeverlee Kasteel Arenberg\u201d, and walk for 1 minute to reach the Thermotechnisch Instituut. You can also take<\/span> Bus 2 (direction Heverlee)<\/span><\/a> from the Leuven Station bus stop number 6 (Leuven Station Perron 6), go down at the stop \u201cHeverlee Kantineplein\u201d, and walk for 3 minutes to reach the Thermotechnisch Instituut. Then follow the signs or staff guidance to rooms.<\/span><\/p>

If you plan to reach by car,<\/strong> you can park your car at Parking De Molen<\/span><\/a><\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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