Colloquium

In this lecture series, leading researchers and promising young scientists report on their latest findings in the field of simulation technologies. We place particular emphasis on the interdisciplinary variety of contributions. We invite all interested parties to our Cluster Colloquium.

All colloquium dates for the Winter Term 2019/20

Bio-inspired Small-Scale Soft Robots

Lecturer: Prof. Metin Sitti

Time
4:00 pm
Room 7.01, Pfaffenwaldring 7
 

Abstract:

Inspired by soft-bodied animals, soft functional active materials could enable physical intelligence for small-scale (from a few millimeters down to a few micrometers overall size) robots by providing them unique capabilities, such as shape changing and programming, physical adaptation, safe interaction with their environment, and multi-functional and drastically diverse dynamics. In this talk, our recent activities on design, manufacturing, and control of new bio-inspired shape-programmable active soft matter and untethered soft swimmers at the milli/microscale are reported. Untethered soft millirobots inspired by spermatozoids, caterpillars, and jellyfishes are proposed using elastomeric magnetic composite materials. Static and dynamic shapes of such magnetic active soft materials are programmed using a computational design methodology. These soft robots are demonstrated to be able to have seven or more locomotion modalities (undulatory swimming, jellyfish-like swimming, water meniscus climbing, jumping, ground walking, rolling, crawling inside constrained environments, etc.) in a single robot for the first time to be able to move on complex environments, such as inside the human body. Preliminary ultrasound-guided navigation of such soft robots is presented inside an ex vivo tissue towards their medical applications to deliver drugs and other cargo locally and heat the local tissues for hyperthermia and cauterization. Next, a more specialized soft-bodied jellyfish-inspired milliswimmer is shown to realize multiple functionalities by producing diverse controlled fluidic flows around its body using its magnetic composite elastomer lappets bent by remote magnetic fields. This jellyfish robot can conduct four different robotic tasks: selectively trap and transport objects of two different sizes, burrow into granular media consisting of fine beads to either camouflage or search a target object, enhance the local mixing of two different chemicals, and generate a desired concentrated chemical path.

Brief Bio:

Metin Sitti received the BSc and MSc degrees in electrical and electronics engineering from Boğaziçi University, Istanbul, Turkey, in 1992 and 1994, respectively, and the PhD degree in electrical engineering from the University of Tokyo, Tokyo, Japan, in 1999. He was a research scientist at University of California at Berkeley, USA during 1999-2002 and a professor in Department of Mechanical Engineering and Robotics Institute at Carnegie Mellon University, USA during 2002-2016. Since 2014, he has been the director of the Physical Intelligence Department at the Max Planck Institute for Intelligent Systems in Stuttgart, Germany. His research interests include small-scale physical intelligence, mobile milli/microrobots, bio-inspiration, advanced soft functional materials, and untethered soft medical devices. He is an IEEE Fellow. He received the ERC Advanced Grant in 2019, Rahmi Koç Science Prize in 2018, SPIE Nanoengineering Pioneer Award in 2011, and NSF CAREER Award in 2005. He received many best paper and video awards in major robotics conferences, e.g., Best Paper Award in the Robotics Science & Systems Conference in 2019. He is the editor-in-chief of Progress in Biomedical Engineering and Journal of Micro-Bio Robotics, associate editor in Extreme Mechanics Letters and Biomimetics & Bioinspiration, and an editorial board member in Advanced Material Technologies and Advanced Intelligent Systems journals.

High-accuracy finite-temperature first principles calculations accelerated by machine learning potentials

Lecturer: Prof. Blazej Grabowski

Time
4:00 pm
Room 7.01, Pfaffenwaldring 7
 

Abstract:

Materials modeling from first principles has recently advanced to a new level. It has become possible to compute materials properties, such as e.g. phase stabilities, with an unprecedented accuracy up to the melting point and even beyond. In this talk, I will give an overview of our respective methodological developments and their applications. These developments combine efficient statistical sampling techniques with concepts from machine learning. They allow us an accurate investigation of highly complex systems including all relevant excitation mechanisms (electrons, fully anharmonic vibrations, magnetism, configurational entropy). I will discuss several applications that are of primary relevance to modern materials design, such as our work on dynamically unstable systems, liquid materials, chemically complex alloys (high entropy alloys), and magnetic materials. Our most recent achievement of calculating high-accuracy temperature-dependent phonon lifetimes will be presented as well. Finally, I will introduce the integrated-development-environment pyiron (www.pyiron.de) into which our developments are presently implemented.

Brief Bio:

Blazej Grabowski received his Diploma degree in physics from the University of Paderborn in 2005. Afterwards, he worked as a PhD student at the Max-Planck-Institut für Eisenforschung in Düsseldorf and received his PhD degree (Dr. rer. nat.) in 2009. He was awarded a scholarship by the excellence academy for young scientists (Nachwuchsakademie) sponsored by the German Research Foundation in 2010. From 2011 to 2012 he worked as a postdoc at the Lawrence Livermore National Lab, USA. From 2012 until 2019 he was head of the ‘Adaptive Structural Materials' group at the Max-Planck-Institut für Eisenforschung in Düsseldorf. In 2019, he became full professor and head of the department of Materials Design in the Institute of Materials Science at the University of Stuttgart. His research interests include finite temperature ab initio modeling, method development, machine learning potentials, diffusion in complex alloys, and study of rare events. He received an ERC Starting Grant in 2015.

Evolving Interfaces in Porous Media

Lecturer: Jun.-Prof. Ph.D. Carina Bringedal

Time
4:00 pm
Room 7.01, Pfaffenwaldring 7
 

Abstract:

Porous media have highly oscillatory characteristics, with rapid micro-scale changes between the solid matrix and the fluid-filled void space. If simulating the micro-scale flow, very detailed domains and fine grids are needed. Usually, when modeling flow and transport through a porous medium, only the averaged behavior is sought and a macro-scale approach is used. In this case, the flow rate depends on the permeability of the porous medium and being able to determine the value of the permeability becomes essential. Through analytical upscaling using homogenization and asymptotic expansions, expressions for the permeability can (under certain assumptions) be found.

Things get more complicated (and more interesting) when there are evolving interfaces involved; that is, either a solid-fluid interface due to a chemical reaction or a fluid-fluid interface due to the porous medium being filled by two immiscible fluids. Then, determining the permeability and other effective properties is not straightforward. In this talk we go through how to mathematically model these evolving interfaces and how to incorporate them in the homogenization. Also, we dig into how to solve the resulting upscaled models numerically.

Massively Parallel & Low Precision Accelerator Hardware as Trends in HPC - How to use it for large scale simulations allowing high computational, numerical and energy efficiency with application to CFD

Lecturer: Prof. Dr. Stefan Turek

Time
4:00 pm
Room 7.01, Pfaffenwaldring 7
 

Abstract:

The aim of this talk is to present and to discuss how modern, resp., future High Performance Computing (HPC) facilities regarding massively parallel hardware with millions of cores together with very fast, but low precision accelerator hardware can be exploited in numerical simulations so that a very high computational, numerical and hence energy efficiency can be obtained. Here, as prototypical extreme-scale PDE-based applications, we concentrate on nonstationary flow simulations with hundreds of millions or even billions of spatial unknowns in long-time computations with many thousands up to millions of time steps. For the expected huge computational resources in the coming exascale era, such type of spatially discretized problems which typically are treated sequentially, that means one time after the other, are still too small to exploit adequately the huge number of compute nodes, resp., cores so that further parallelism, for instance w.r.t. time, might get necessary.

In this context, we discuss how "parallel-in-space simultaneous-in-time" Newton-Multigrid approaches can be designed which allow a much higher degree of parallelism. Moreover, to exploit current accelerator hardware in low precision (for instance, GPUs or TPUs), that means mainly working in single precision or even half precision, we discuss the concept of "prehandling" (in contrast to "preconditioning") of the corresponding ill-conditioned systems of equations, for instance arising from Poisson-like problems. Here, we assume a transformation into an equivalent linear system with similar sparsity but with much lower condition numbers so that the use of low-precision hardware might get feasible. In our talk, we provide for both aspects preliminary numerical results as "proof-of-concept" and discuss the open problems, but also the challenges, particularly for incompressible flow problems.

All colloquium dates for the Summer Term 2019

Let's go high-dimensional!

Lecturer: Prof. Dr. rer. nat. Dirk Pflüger, University of Stuttgart

Time
4:00 pm
Room 7.01, Pfaffenwaldring 7
 

Abstract:

Today, simulations are indispensable in most research fields. Especially large-scale problems and multi-X settings require a joint effort from their application sciences, mathematics, and computer science. At latest if data and parameters come into play, higher-dimensional questions are omnipresent: How does uncertain knowledge about parameters affect simulation results? Which properties of a material lead to optimal behavior? And what if our knowledge consists purely of a few measurements? Such problems become quickly infeasibly expensive so that many classical methods fail.

In this talk, we show some surprising properties of high dimensionalities and recent research on SParse Grids, a method to tackle higher-dimensional problems. We show how the interplay of numerical algorithms, efficient data structures and high-erformance computing enable us to solve problems ranging from the optimization via muscle modeling to data mining and uncertainty quantification.

Bio:

Dirk Pflüger studied Computer Science at the University of Stuttgart (with minor subject Music Theory at the Conservatory of Music) and Information Technology at the University of Sydney. For his PhD in Scientific Computing he joined the Technical University of Munich. In 2010, he received his PhD for his work on adaptive, high-dimensional approximations.

Following a short time as a postdoc at TUM and the Australian National University, Dirk Pflüger was appointed as SimTech Juniorprofessor with Tenure Track at the University of Stuttgart in 2012, where he joined the Institute for Parallel and Distributed Systems. Since 2015, he is fellow of the Junge Akademie, the world's first national academy of young scientists, at the Berlin-Brandenburg Academy of Sciences and Humanities and the German National Academy of Sciences Leopoldina. In 2016, he was nominated as fellow of the Stuttgart Center for Simulation Science. Since 2018, he is full professor, following a successful evaluation of his juniorprofessorship.

His research in scientific computing focuses on adaptive numerical methods for high-dimensional problems and simulations, data-driven problems and numerical data mining, their efficient implementation in high-performance computing, hardware-aware algorithms and fault-tolerance.

Learning to control redundant musculoskeletal systems - arm and leg robot and computer simulation

Lecturer: Prof. Dr. rer. nat. Dipl.-Phys. Syn Schmitt, University of Stuttgart

Time
6:00 pm
Room 7.01, Pfaffenwaldring 7
 

Abstract:

Biological motion is fascinating in almost every aspect you look upon it. Especially locomotion plays a crucial part in the evolution of life. The fitness of species depends on movement speed, acceleration, maneuverability, endurance, etc. and decides the vital game between predator and prey and finally whether new offspring comes to life. Even nowadays, reduced movement capabilities, e.g. a broken femur following a fall, can have fatal consequences. Structures, like the bones connected by joints, soft and connective tissues and contracting proteins in a muscle-tendon unit enable and prescribe the respective species' specific locomotion patterns. Most importantly, biological motion is autonomously learned, it is untethered as there is no external energy supply like, for example, an air reservoir or electric plug, and typically in vertebrates, it's muscle-driven. This talk is focused on human motion. Digital models and biologically inspired robots are presented, built for a better understanding of biology’s complexity. Modeling musculoskeletal systems reveals that the mapping from muscle stimulations to movement dynamics is highly nonlinear and complex, which makes it difficult to control those systems with classical techniques. It is not only investigated whether machine learning approaches are capable of learning a controller for such systems. It is also of interest whether or not, the structure of the musculoskeletal apparatus exhibits properties that are favorable for the learning task. Experiments on a simulated musculoskeletal model of a human arm and leg and real biomimetic muscle-driven robots show that it is possible to learn an accurate controller despite high redundancy and nonlinearity, while retaining sample efficiency. Thus, the basic understanding of the interplay between the enormous powerful human brain and the ingenious evolutionary design is enhanced. From an application point of view, this work is motivated by learning to control musculoskeletal systems.

Bio:

Syn Schmitt studied physics at the University of Stuttgart and graduated from the University of Tuebingen with a PhD in computational biophysics (topic: muscle modelling). In 2008, Schmitt was associated with the Stuttgart Research Centre for Simulation Technology (SimTech). He was appointed as Juniorprofessor (assistant professor) at the University of Stuttgart in 2012. In 2016, he received the fellowship of the Stuttgart Center for Simulation Science (SimTech). Since 2018, he is full professor of "Computational Biophysics and Biorobotics" at the University of Stuttgart and in 2019 he founded the Institute for Modelling and Simulation of Biomechanical Systems, together with his colleague Oliver Roehrle. Syn Schmitt is a faculty member of the International Max Planck Research School for Intelligent Systems (IMPRS-IS), a member of the German Physicist Society (DPG) and the German Association of Applied Mathematics and Mechanics (GAMM). His research focusses on autonomous muscle-driven motion with special interests on design principles of the locomotion apparatus, non-linear dynamics of locomotion, motor control and morphological computation in biological and technical systems.

Everyday Gaze Sensing and Analysis for Cognitive User Interfaces

Lecturer: Prof. Dr. Andreas Bulling, University of Stuttgart

Time
4:00 pm
Room 7.01, Pfaffenwaldring 7
 

Abstract:

As human-computer interfaces become more sophisticated, they increasingly implement or imitate human perceptual, learning, and interactive skills. This gives rise to a new research area on cognitive user interfaces as the intersection of human-computer interaction, computer vision, and machine learning. Human gaze is particularly promising for this new class of interfaces given its fundamental importance in human social communication, cognition, and intelligence and its close links to cognition and visual perception. However, current cognitive user interfaces only scratch the surface of the vast information contained in gaze, particulalry in unconstrained daily-life settings. My research focuses on next-generation user interfaces that fully leverage human gaze to enable, support, and enhance human-machine interactions.

In this talk I will present selected examples of my research towards this vision with a particular focus on computer vision methods for everyday gaze sensing as well as machine learning methods for gaze analysis and eye-based user modelling.

Bio:

Andreas Bulling is Full Professor (W3) of Computer Science at the University of Stuttgart where he holds the chair for Human-Computer Interaction and Cognitive Systems. He is also Faculty at the International Max Planck Research School for Intelligent Systems (IMPRS-IS) and Member in the Cluster of Excellence "Data-integrated Simulation Science" (SimTech). He received his MSc. (Dipl.-Inform.) in Computer Science from the Karlsruhe Institute of Technology (KIT), Germany, focusing on embedded systems, robotics, and biomedical engineering. He holds a PhD in Information Technology and Electrical Engineering from the Swiss Federal Institute of Technology (ETH) Zurich, Switzerland. Andreas was previously a Feodor Lynen Research Fellow and a Marie Curie Research Fellow in the Computer Laboratory at the University of Cambridge, UK, a postdoctoral research associate in the School of Computing and Communications at Lancaster University, UK, as well as a Junior Research Fellow at Wolfson College, Cambridge. From 2013 – 2018 he was a Senior Researcher at the Max Planck Institute for Informatics and an Independent Research Group Leader (W2) at the Cluster of Excellence on Multimodal Computing and Interaction (MMCI) at Saarland University. Andreas is UbiComp steering committee member and serves on the editorial boards of the Proceedings of the ACM on Interactive, Mobile, Wearable, and Ubiquitous Technologies, ACM Transactions on Interactive Intelligent Systems, and the Journal of Eye Movement Research. He served as co-chair, TPC member, and reviewer for major conferences, most recently as TPC co-chair for ACM UbiComp 2016 and IEEE PerCom 2015, associate chair for ACM ETRA 2016 and 2018 as well as ACM CHI 2013, 2014, 2018, and 2019, and general chair for ETRA 2020. He received an ERC Starting Grant in 2018.

All colloquium dates for the Winter Term 2018/19

Smart Sensors as Multiphysics Problems - Why Modeling and Simulations are Crucial for Successful In-Situ Measurements

Referent: Prof. Dr. Jens Anders, University of Stuttgart

Time
4:00 pm
Auditorium 7.01, Pfaffenwaldring 7
 

Abstract of the presentation:

Smart sensors in the broadest sense are sensors that are combined with dedicated hardware or software to produce a performance and/or functionality that goes greatly beyond that of the raw sensor. As such, smart sensors bear the potential to revolutionize all aspects of our everyday life, ranging from smart homes that benefit from ambient assisted living, optimized production lines in the frame of Industry 4.0 to high-end sensors that enable entirely new experiments in the scientific world.
In this talk, we will investigate the use of smart sensors in the latter field of high-end sensing for scientific applications and discuss how the multiphysics and advanced modeling aspects of state-of-the-art smart sensors are crucial for their performance.
To this end, we will discuss examples of biomedical quantum sensors that greatly benefit from the embedding of the interface electronics for enhanced performance. It will be discussed how an advanced modeling of the sensor together with a precise modeling of the interface electronics as nonlinear dynamical system can be used for co-designing sensor and electronics to improve the overall system performance. Finally, we will talk about the challenges in numerical simulations of such advanced sensor systems, which arise from the immense precision (often precisions of 10-9 or better are required) that is frequently required in the scientific context.

A finite-difference/finite-element framework for fluid-structure interaction using an immersed boundary method with variational transfer

Referent: Prof. Dr. Dominik Obrist, ARTORG Center for Biomedical Engineering Research, University of Bern

Time
4:00 pm
Auditorium 7.01, Pfaffenwaldring 7

Abstract of the presentation:

The flow systems of the heart and the great blood vessels comprise complex materials (soft tissue) and flows at moderately high Reynolds numbers which may undergo transition from laminar to turbulent flow. Computational modelling of such fluid-structure interaction (FSI) problems requires efficient high-fidelity solvers for structure and flow as well as a robust scheme for coupling the two phases.

We present a new FSI framework which comprises a finite-element solver for the full elastodynamics equations of the structure and a Navier-Stokes solver using high-order finite-difference schemes. The interaction between fluid and structure is modelled by an immersed boundary ansatz which uses a variational scheme for transferring data between the structural mesh and the Cartesian fluid grid.

Mixed-dimensional modeling and simulation

Referent: Prof. Dr. Jan Martin Nordbotten, University of Bergen

Time
4:00 pm
Auditorium 7.01, Pfaffenwaldring 7

Abstract of the presentation:

We provide an overview of mixed-dimensional physical processes, and the overarching principles governing the construction of consistent and well-posed mathematical models for these systems. We provide some mathematical structure, and show how this leads to a natural development of numerical methods, analogous to the case for classical (fixed-dimensional) problems. 

Micro-Macro Models for Reactive Flow and Transport Problems in Complex Media

Referent: Prof. Dr. Peter Knabner, University of Erlangen-Nürnberg

Time
4:00 pm
Auditorium 7.01, Pfaffenwaldring 7

Abstract of the presentation:

In porous media and other complex media with different length scales, (periodic) homogenization has been successfully applied for several decades to arrive at macroscopic, upscaled models, which only keep the microscopic information by means of a decoupled computation of “effective” parameters on a reference cell. The derivation of Darcy’s law for flow in porous media is a prominent example. Numerical methods for this kind of macroscopic models have been intensively discussed and in general are considered to be favourable compared to a direct microscale computation. On the other hand, if the interplay of processes becomes too complex, e.g. the scale seperation does not act in a proper way, the porous medium itself is evolving, ..., the upscaled models obtained may be micro-macro models in the sense, that the coupling of the macroscopic equations and the equations at the reference cell is both ways, i.e. at each macroscopic point a reference cell is attached and the solution in the reference cell depends on the macroscopic solution (at that point) and the macroscopic solution depends on the microscopic solutions in the reference cells. At the first glance such models seem to be numerically infeasible due to their enormous complexity ( in d+d spatial variables). If on the other hand this barrier can be overcome, micro-macro models are no longer a burden but a chance by allowing more general interaction of processes (evolving porous media, multiphase flow, general chemical reactions, ...), where the microscopic processes “compute” the constitutive laws, which need longer be assumed (similar to the concept of heterogeneous homogenization). We will discuss various examples and in particular numerical approaches to keep the numerical complexity in the range of pure macroscopic models.

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Sabine Sämisch
 

Sabine Sämisch

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