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DHPC User Summit 2024

Unlocking the Power of DelftBlue Join us for the DHPC User Summit 2024 , a premier event dedicated to showcasing the innovative research and projects powered by the DelftBlue supercomputer. Whether you're a student, researcher, or professional, this summit offers a unique opportunity to learn, network, and collaborate with experts in high-performance computing. About the Event The DHPC User Summit brings together the vibrant community of users and developers working with the DelftBlue supercomputer. This year's event focuses on the groundbreaking research conducted using DelftBlue, offering attendees a chance to delve into the technical achievements and the impact of high-performance computing across various fields. What to Expect Inspiring Talks: Hear from several distinguished guest speakers, including industry leaders like SURF, as they share their experiences and insights on leveraging the power of DelftBlue for research and innovation. Networking Lunch: Connect with fellow attendees, exchange ideas, and explore potential collaborations during the networking lunch. Panel Discussion: Engage in a lively panel discussion that addresses the current challenges and future opportunities in supercomputing. Pub Quiz & Prizes: Wrap up the day with drinks and a fun pub quiz, where the best team will take home an exciting prize! Who Should Attend? This event is open to everyone interested in high-performance computing, from master students eager to learn more about the field to seasoned researchers looking to expand their knowledge and network with peers. Registration Please click the button below to register for the DHPC User Summit 2024. Don't miss this opportunity to be part of an inspiring day filled with learning, networking, and fun! We do not charge a fee for the DelftBlue user summit. If you register, we expect you to be there. If you are unable to attend, please cancel as soon as possible. The room capacity is limited to 100 people. You can cancel your participation via the confirmation e-mail that you will receive from Aanmelder after you have registered. Programme Speakers Topics Location 09:00 - 09:30 | Walk-in (Welcome) 09:30 - 10:00 | Opening: What's new on DelftBlue? - Jonas Thies 10:00 - 10:30 | Open OnDemand | Simplifying access to DelftBlue - Sören Wacker 10:30 - 11:00 | Break 11:00 - 11:30 | DEM Simulations for Industrial Granular Flows: Balancing Efficiency and Accuracy - Raïsa Roeplal 11:30 - 11:45 | Sudoku Challenge - Mate Cser 11:45 - 12:00 | Meet the DelftBlue Contact Group - Luis Laguarda Sanchez 12:00 - 13:15 | Lunch 13:15 - 13:30 | TBD 13:30 - 13:50 | HPC: Current and future challenges - Valeriu Codreanu (SURF) 13:50 - 14:50 | Panel Discussion: The Future of HPC Panelists: Valeriu Codreanu (SURF), (Fujitsu), Alan Hanjalic (AI), Gerhard Wellein (International) | Moderator: Matthias Moller 14:50 - 15:20 | Break 15:20 - 15:40 | Developing a Graph Neural Network Surrogate Model for Accelerating Multiscale Simulations - Joep Storm 15:40 - 16:00 | Environmental Flows with DelftBlue - Clara Garcia Sanchez 16:00 - 16:20 | Talk by Martin van Gijzen 16:20 - 18:00 | Network drinks & PubQuiz by our quizmaster Dennis Palagin Sören Wacker Valeriu Codreanu Martin van Gijzen Jonas Thies Joep Storm Raïsa Roeplal Clara García-Sánchez Jonas Thies What's new on DelftBlue? Jonas Thies has a Bachelor degree in Computational Engineering (Erlangen 2003), a Master in Scientific Computing (KTH Stockholm, 2006) and a PhD in applied mathematics (Groningen 2011). He spent two years at the Center for Interdisciplinary Mathematics in Uppsala, after which he moved to Cologne as a Scientific Employee of the German Aerospace Center (DLR) Institute for Software Technology. There he led a research group on parallel numerics from 2017 to 2021. Since June 2021 he is an Assistant Professor at the Delft High Performance Computing Center DHPC. Sören Wacker Open OnDemand | Simplifying access to DelftBlue Sören Wacker is a Senior Research HPC Engineer in the newly formed Research Engineering and Infrastructure Team (REIT) at TU Delft, where he specializes in Data Engineering, Machine Learning, and Full-Stack Development. He completed his PhD at the Max Planck Institute for Biophysical Chemistry (now the Max Planck Institute for Multidisciplinary Science) and the University of Göttingen in Germany. His professional journey includes significant experience at the Center for Molecular Simulations and the Ian Lewis Research Group in Canada, where he contributed to the large-scale applied research project ResistanceDB. He has also collaborated with several Canadian startups in the fields of drug development and machine learning. Currently, Sören supports the CropXR project, a 100 million Euro initiative focused on advancing crop research over the next decade. His work reflects a commitment to leveraging advanced computational methods to solve complex challenges. Outside of his professional endeavors, he enjoys practicing contact improvisation, which complements his holistic approach to life and work. Raïsa Roeplal DEM Simulations for Industrial Granular Flows: Balancing Efficiency and Accuracy Raïsa Roeplal earned her master’s degree in Mechanical Engineering from the University of Twente in 2016. During her studies, she investigated the mixing of cohesive particles in paddle mixers and developed a keen interest in the segregation behaviour of granular materials. In 2020, she joined the GranChaMlab at the TU Delft’s Mechanical Engineering faculty to pursue her PhD. Her research, part of the Industrial Dense Granular Flows (IDGF) project, focuses on developing a Discrete Element Method (DEM) model to analyse the flow and packing behaviour of blast furnace mixtures during charging. This simulation requires significant computational power to track the motion of millions of particles over time to predict their segregation behaviour. The model aims to enhance the charging process, ultimately reducing the carbon emissions associated with blast furnace ironmaking. Go to her personal page . Abstract The Discrete Element Method (DEM) is a computational technique for simulating granular flows. Fundamentally, it employs the equations of motion to predict the interactions of individual particles with their environment, enabling the prediction of flow behaviour over time. To effectively apply DEM to real-world industrial challenges, simulations must represent vast numbers of particles with realistic morphological characteristics. This demands substantial computational power, and creating a model often involves balancing efficiency and accuracy. This presentation will showcase examples from actual industrial applications. Valeriu Codreanu HPC: Current and future challenges Valeriu studied Electrical Engineering and got his MSc at the Polytechnic University of Bucharest. He followed-up with a PhD in Computer Architecture at the same institute, graduating in 2011 after a research stay at TU Delft. Valeriu continued as a researcher at Eindhoven University of Technology and University of Groningen, working on GPU computing, computer vision, and embedded systems. In 2014, he joined SURFsara as an HPC consultant and in 2016 became the PI of an Intel Parallel Computing Center project on ‘Scaling up Deep Learning’, subsequently leading the Machine Learning team at SURF. Valeriu is currently leading the High-Performance Computing and Visualization group at SURF, responsible for the Dutch National Supercomputing infrastructure Abstract High-Performance Computing (HPC) has become essential to scientific research, engineering, and industry, driving advancements in areas like climate modeling, genomics, and AI. This talk will examine the current state of HPC, highlighting recent technological advancements and emerging trends such as exascale computing and AI integration. We will also discuss the importance of developing coherent multi-tier HPC infrastructures, where European, national and regional centers work in harmony to provide scalable and accessible computing resources. Looking ahead, key challenges include managing complex architectures, ensuring energy efficiency, and fostering collaboration across these tiers. Additionally, we’ll explore the growing need for skilled professionals and global cooperation to address the future demands of HPC. Joep Storm Developing a Graph Neural Network Surrogate Model for Accelerating Multiscale Simulations Joep Storm is a PhD candidate at the Delft University of Technology in the faculty of Civil Engineering and Geosciences. He works in the Computational Mechanics research group, which focuses on developing computational models for simulating the mechanical behaviour of materials and structures. He is also part of the SLIMM AI Lab, which uses advances in statistical learning to combine probabilistic machine learning and multiscale solid mechanics. Joep’s main research interest lies in accelerating multiscale mechanical models, such as those of advanced polymer composite materials, using data-driven techniques. These techniques include surrogate modelling, active learning, graph neural networks, and uncertainty quantification, aiming to enable the practical use of multiscale models for more efficient structural designs. In 2024 he was a visiting scholar at Columbia University in New York, at the Theoretical and Computational Poromechanics research group. Joep’s page can be found here . Abstract Simulating the mechanical response of advanced materials can be done more accurately using multiscale models than with single-scale simulations. However, the computational costs stand in the way of the practical application of this approach. We introduce a surrogate modeling strategy that aims to reduce this cost while retaining all microscopic information. We achieve this using a graph neural network for elasto-plastic materials. We embed a physics-based model inside the graph neural network architecture, which increases the computational cost but improves the accuracy of the surrogate model predictions. As the computation time of our method scales favorably with the number of elements in the microstructure compared to the finite element method, our method can significantly accelerate multiscale simulations of advanced materials. Clara Garcia Sanchez Environmental Flows with DelftBlue I'm part of the 3D geoinformation group at TU Delft, thanks to the Delft Technology Fellowship. My research is focused on environmental problems, explicitly addressing dispersion and airflow predictions in the built environment. I am interested in renewable energies, as well as mitigation techniques that can allow to prevent and reduce urban pollution. Part of my research focuses on applying uncertainty quantification methodologies to predict winds in the urban canopy. During my career, I have gathered experience with lowand high-fidelity approaches within computational fluid dynamics and dealt with experimental data. Although my background is in aerospace engineering, I steered toward environmental flows later in my career. Before joining TUDelft, I was a postdoctoral research scientist at the Carnegie Institute for Science in Dr. Caldeira’s lab. Before joining the Carnegie Institute for Science, I graduated with my PhD at the University of Antwerp in collaboration with the von Karman Institute for Fluid Dynamics. My PhD research focused on quantifying inflow uncertainties for CFD dispersion simulations in the atmospheric boundary layer. Abstract Predicting environmental flows has become increasingly vital as we tackle climate change and push for sustainable solutions in our ecosystems. In this talk, we'll explore fascinating research on how local roughness impacts flow. First, we'll dive into the intricate effects of coral shapes on nutrients and mass transport using cutting-edge high-fidelity simulations (DNS). Then, we'll shift gears to the future of urban air mobility, analyzing how winds in complex urban areas pose risks to air transport—and how the same data can improve wind safety for pedestrians at street level. Exciting insights into both nature and urban design await! Martin van Gijzen Closing session Martin van Gijzen received Master's and Ph.D. degrees in Applied Mathematics from the Delft University of Technology in 1989 and 1993, respectively. He carried out his Ph.D. research at TNO Building and Construction Research, now DIANA FEA . In 1994, he became a Postdoctoral Researcher in Mathematics at Utrecht University where he developed parallel algorithm to simulate global ocean circulation. In 1997, he joined the research staff at TNO Physics & Electronics Laboratorium , where he was a project leader on underwater acoustics. In 2002 he accepted a position as a Senior Scientist in Parallel Algorithms at CERFACS in Toulouse , France. In 2004, he joined the faculty of the Delft Institute of Applied Mathematics at the Delft University of Technology , where he is now a Professor in High-Performance Computing. He recently succeeded Kees Vuik as scientific director of DHPC. The DelftBlue user summit takes place in X (building 37) on the TU Delft campus. Register now DHPC User Summit 2024 Date: 08-11-2024 Location: Delft X, Building 37 Mekelweg 8 NL-2628 CD Delft Costs: Free Note: -

Responsible Design and Engineering of Human-centered AI and Data driven Systems

Responsible Design and Engineering of Human-centered AI and Data Driven Systems AI, data and digitalisation are increasingly essential to solving big societal issues. At TU Delft, we do not only want AI and data driven systems to make human activities more efficient and sustainable, but these systems should be designed in such a way that they also make them fairer, more democratic and even lead to a greater human wellbeing. Focus on humans and engineering together When designing and engineering technological solutions to contemporary challenges, we should not lose sight of the human being. AI and data driven systems should contribute to greater safety, freedom and fairness, and therefore to greater physical and psychological wellbeing of all people. Humans are also the focus in terms of how they engage with the technology. For example, we ask ourselves: What are specific human versus technological strengths? And how can humans and technology optimally cooperate? To empower people To properly serve and engage humans, systems need to be properly designed and engineered. In the context of AI and data driven systems, this covers aspects related to algorithms, to data, and to whole systems and their interfaces. We strive for systems designed in such a way that the people using it, really understand the systems. And the systems observe, interpret and understand, at least to some degree, human behaviour. The way we work For many years already, researchers at TU Delft have been developing the scientific, technological and methodological capabilities that are needed to realise this vision of a truly human-centered approach to AI systems. We consider human-centric AI to be inherently transdisciplinary, as it brings together science and practice in computer science, design, systems engineering, human-machine interaction, psychology, ethics and philosophy, organisation, and an entire range of application-specific disciplines. News More news Events More events TU Delft Digital Ethics Centre Together with government agencies and companies, the TU Delft Digital Ethics Centre bridges the gap between abstract ethical discussions on these values and concrete digital innovations. The Digital Ethics Centre is led by Jeroen van den Hoven, professor of Ethics and Technology and Catholijn Jonker, professor of Interactive Intelligence. www.tudelft.nl/digital-ethics-centre Human Language Technologies Human language is an important medium of interactions between people and AI, and a primary source of the data that AI interprets. The TU Delft community around human language technologies (HLT) is led by Jie Yang, Assistant Professor at the Web Information Systems group, aiming to advance the design, development, governance, and use of the next generation of HLT. Read more on Human Language Technologies Centre for Meaningful Human Control The centre for meaningful human control connects academics and practitioners aiming to conceptualise, design, implement, and assess systems under meaningful human control. We strive to be a lighthouse for collaboration among multiple stakeholders, while leveraging interdisciplinary expertise, existing initiatives at TU Delft, and an international network of collaborators at the forefront of research and practice on meaningful human control. Read more on Centre for Meaningful Human Control Human-Centered AI Systems The research community around Human-Centered AI Systems, also based on the TU Delft AI Labs that deal with human-centered AI is led by Alessandro Bozon, professor Human Centered AI. This community is concerned with research and teaching about understanding, design, and engineering of AI behaviour. Lees meer over Human-Centered AI Systems Hybrid Intelligence Our TU Delft activities on Hybrid Intelligence are led by Catholijn Jonker, professor of Interactive Intelligence. The goal here is to design hybrid intelligent systems, an approach to AI that puts humans at the centre, changing the course of the ongoing AI revolution. www.hybrid-intelligence-centre.nl/ AiTech Institute The AITech Institute is concerned with the line of research around Meaningful Human Control of Autonomous Intelligent Systems and is led by David Abbink, professor of Haptic Human-Robot Interaction. www.tudelft.nl/aitech Delft Design for Values Institute A key component of human-centered AI is value driven design. The Delft Design for Values Institute (DDfV) carries this line of research and is led by Jeroen van den Hoven, professor of Ethics and Technology. www.delftdesignforvalues.nl/ TU Delft AI Labs This TU Delft AI Labs are closely involved in research and education in the field of responsible design of human-centric AI and data-driven systems: Contact Wetenschappelijke contactpunten David Abbink Alessandro Bozzon Jeroen van der Hoven Catholijn Jonker Community Christine Bel +31 15 27 88335 C.I.Bel@tudelft.nl Innovation Manager AI Helma Dokkum W.M.Dokkum@tudelft.nl Business developer AI & Digital Ethics Charlotte Boelens +31 15 27 81269 C.L.T.Boelens@tudelft.nl Community manager TU Delft AI Labs & Talent Programma Taylor Stone T.W.Stone-1@tudelft.nl Community & Programme Manager AI Research Themes Lieke Muller E.J.Muller-1@tudelft.nl Communication Manager AI Initiative Marc de Kool +31 15 27 83723 A.J.M.deKool@tudelft.nl Science Information Officer Digital Society

Software Carpentry workshops

Software Carpentry workshops 11 November 2024 09:00 till 14 November 2024 13:30 - Location: TU Delft Library, Orange Room | Add to my calendar Registration for PhD candidates Registration for other TU Delft associates Course Description Software Carpentry is a four half-day hands-on workshop, which focuses on helping researchers develop foundational computational skills to get started with programming. In this workshop, you also get introduced to best practices for working with code in a reproducible way. A software carpentry workshop is your first step in learning computational and digital skills, which you will be able to use and apply in academic and non-academic settings in your future. Training takes place Monday 11 November - Thursday 14 November 2024 - in person (at TU Delft Library - Orange Room) - 09:00 - 13:30 hrs each day Target Audience This workshop is useful for all PhD candidates and researchers with very little or no previous programming experience and would like to start learning digital and programming skills. The software carpentry workshops are the perfect space to overcome the initial barriers when learning programming skills. It is useful for those researchers interested in getting an overview and an introduction to relevant tools to start working with code and provide the necessary elements to follow more advanced courses. Prerequisites This workshop is useful for all PhD candidates and researchers with very little to no prior computational experience who are working with tabular data. This is a basic/introductory course. You will need to allocate approximately 2 hours of preparatory work before the first class of the workshop in order to: fill in a pre-workshop survey to help the instructor to get an overview of the learners previous experience with programming and adjust content and pace accordingly (you will receive an email with the link to the survey). install the software and download the datasets that you will use during the workshop (you will receive an email with the detailed instructions before the workshop). More information You can reach the organisers with questions at RDMtraining-lib@tudelft.nl .

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New LDE trainee in D&I office

Keehan Akbari has started since the beginning of September as a new LDE trainee in the Diversity and Inclusion office. What motivated him to work for the D&I office, what does he expect to achieve during this traineeship? Read the short interview below! What motivated you to pursue your LDE traineeship in Diversity and Inclusion office of the TU Delft? I completed both bachelor's and master's degrees in Cultural Anthropology and Development Sociology at Leiden University. Within these studies, my main area of interest was in themes of inclusion and diversity. After being hired as a trainee for the LDE traineeship, and discovering that one of the possible assignments belonged to the Diversity and Inclusion office, my choice was quickly made. I saw this as an excellent opportunity to put the theories I learned during my studies into practice. What specific skills or experiences do you bring to the D&I office that will help promote inclusivity on campus? I am someone who likes to connect rather than polarize, taking into account the importance of different perspectives and stakeholders. I believe that this is how one can achieve the most in fostering diversity and inclusion. You need to get multiple parties on board to get the best results. What are your main goals as you begin your role here, and how do you hope to make an impact? An important goal for me this year is to get students more involved in diversity and inclusion at the university. One way I will try to accomplish this is by contributing to the creation of D&I student teams. By establishing a D&I student team for faculties, it will be possible to deal with diversity- and inclusion-related issues that apply and relate to the specific department. How do you plan to engage with different (student) communities within the university? Since I am new to TU Delft, the first thing I need to do is expand my network here. Therefore, I am currently busy exploring the university and getting to know various stakeholders. Moreover, I intend to be in close contact with various student and study organizations to explore together how to strengthen cooperation on diversity and inclusion. Welcome to the team Keehan and we wish you lots of success with your traineeship!

Researchers from TU Delft and Cambridge University collaborate on innovative methods to combat Climate Change

For over a year and a half, researchers from TU Delft and the Cambridge University Centre for Climate Repair have worked together on groundbreaking techniques to increase the reflectivity of clouds in the fight against global warming. During a two-day meeting, the teams are discussing their progress. Researchers at Cambridge are focusing on the technical development of a system that can spray seawater, releasing tiny salt crystals into the atmosphere to brighten the clouds. The team from TU Delft, led by Prof. Dr. Ir. Herman Russchenberg, scientific director of the TU Delft Climate Action Program and professor of Atmospheric Remote Sensing, is studying the physical effects of this technique. Prof. Russchenberg emphasizes the importance of this research: "We have now taken the first steps towards developing emergency measures against climate change. If it proves necessary, we must be prepared to implement these techniques. Ideally, we wouldn't need to use them, but it's important to investigate how they work now." Prof. Dr. Ir. Stefan Aarninkhof, dean of the Faculty of Civil Engineering and Geosciences, expresses pride in the team as the first results of this unique collaboration are becoming visible. If the researchers in Delft and Cambridge can demonstrate the potential of the concept, the first small-scale experiments will responsibly begin within a year. This research has been made possible thanks to the long-term support from the Refreeze the Arctic Foundation, founded by family of TU Delft alumnus Marc Salzer Levi . Such generous contributions enable innovative and high-impact research that addresses urgent global challenges like climate change. Large donations like these enable the pursuit of innovative, high-impact research that may not otherwise be feasible, demonstrating how our collective effort and investment in science can lead to real, transformative solutions for global challenges like climate change. Climate-Action Programme

How system safety can make Machine Learning systems safer in the public sector

Machine Learning (ML), a form of AI where patterns are discovered in large amounts of data, can be very useful. It is increasingly used, for example, in chatbot Chat GPT, facial recognition, or speech software. However, there are also concerns about the use of ML systems in the public sector. How do you prevent the system from, for example, discriminating or making large-scale mistakes with negative effects on citizens? Scientists at TU Delft, including Jeroen Delfos, investigated how lessons from system safety can contribute to making ML systems safer in the public sector. “Policymakers are busy devising measures to counter the negative effects of ML. Our research shows that they can rely much more on existing concepts and theories that have already proven their value in other sectors,” says Jeroen Delfos. Jeroen Delfos Learning from other sectors In their research, the scientists used concepts from system safety and systems theory to describe the challenges of using ML systems in the public sector. Delfos: “Concepts and tools from the system safety literature are already widely used to support safety in sectors such as aviation, for example by analysing accidents with system safety methods. However, this is not yet common practice in the field of AI and ML. By applying a system-theoretical perspective, we view safety not only as a result of how the technology works, but as the result of a complex set of technical, social, and organisational factors.” The researchers interviewed professionals from the public sector to see which factors are recognized and which are still underexposed. Bias There is room for improvement to make ML systems in the public sector safer. For example, bias in data is still often seen as a technical problem, while the origin of that bias may lie far outside the technical system. Delfos: “Consider, for instance, the registration of crime. In neighbourhoods where the police patrol more frequently, logically, more crime is recorded, which leads to these areas being overrepresented in crime statistics. An ML system trained to discover patterns in these statistics will replicate or even reinforce this bias. However, the problem lies in the method of recording, not in the ML system itself.” Reducing risks According to the researchers, policymakers and civil servants involved in the development of ML systems would do well to incorporate system safety concepts. For example, it is advisable to identify in advance what kinds of accidents one wants to prevent when designing an ML system. Another lesson from system safety, for instance in aviation, is that systems tend to become more risky over time in practice, because safety becomes subordinate to efficiency as long as no accidents occur. “It is therefore important that safety remains a recurring topic in evaluations and that safety requirements are enforced,” says Delfos. Read the research paper .