We encounter simulations unconsciously in many everyday situations: the daily weather forecast, non-destructive crash tests for car approval, lightweight and material-saving plastic parts in household appliances, or investment strategies for funds and pension investors... An interdisciplinary team has set itself the task of bringing the topics of simulation, mathematical modelling, and artificial intelligence to schools. How do we recognise simulations? How are the results to be understood? And what do employees in data centres work on? How does AI work? Simulated Worlds answers these questions with many practical examples.
In order to implement a sustainable and professional approach to the development and management of energy-optimized research software, suitable support structures and services operated by highly specialized research software engineers are required. This is where the new state service bwRSE4HPC comes in.
The goal of this project is to use deep neural networks as building blocks in a numerical method to solve the Boltzmann equation. This is a particularly challenging problem since the equation is a high-dimensional integro-differential equation, which at the same time possesses an intricate structure that a numerical method needs to preserve. Thus, artificial neural networks might be beneficial, but cannot be used out-of-the-box. We follow two main strategies to develop structure-preserving neural network-enhanced numerical methods for the Boltzmann equation. First, we target the moment approach, where a structure-preserving neural network will be employed to model the minimal entropy closure of the moment system. By enforcing convexity of the neural network, one can show, that the intrinsic structure of the moment system, such as hyperbolicity, entropy dissipation and positivity is preserved. Second, we develop a neural network approach to solve the Boltzmann equation directly at discrete particle velocity level. Here, a neural network is employed to model the difference between the full non-linear collision operator of the Boltzmann equation and the BGK model, which preserves the entropy dissipation principle. Furthermore, we will develop strategies to generate training data which fully sample the input space of the respective neural networks to ensure proper functioning models.
The aim of this project is to strengthen research-oriented teaching, especially in the areas of AI, machine learning, simulation and modeling, by providing a state-wide service bwJupyter.
In the Research Training Group (RTG) "Tailored Scale-Bridging Approaches to Computational Nanoscience" we investigate problems, that are not tractable by computational chemistry standard tools. The research is organized in seven projects. Five projects address scientific challenges such as friction, materials aging, material design and biological function. In two further projects, new methods and tools in mathematics and computer science are developed and provided for the special requirements of these applications. The SCC is involved in projects P4. P5 and P6.
Waves are everywhere, and understanding their behavior leads us to understand nature. The goal of CRC 1173 »Wave Phenomena« is therefore to analytically understand, numerically simulate, and eventually manipulate wave propagation under realistic scenarios by intertwining analysis and numerics.
CAMMP stands for Computational and Mathematical Modeling Program. It is an extracurricular offer of KIT for students of different ages. We want to make the public aware of the social importance of mathematics and simulation sciences. For this purpose, students actively engage in problem solving with the help of mathematical modeling and computer use in various event formats together with teachers. In doing so, they explore real problems from everyday life, industry or research.
The Simulated Worlds project aims to provide students in Baden-Württemberg with a deeper critical understanding of the possibilities and limitations of computer simulations. The project is jointly supported by the Scientific Computing Center (SCC), the High Performance Computing Center Stuttgart (HLRS) and the University of Ulm and is already working with several schools in Baden-Württemberg.
Together with partners at Forschungszentrum Jülich and Fritz Haber Institute Berlin, our goal is to develop a novel intelligent management system for electric batteries that can make better decisions about battery charging cycles based on a detailed surrogate model ("digital twin") of the battery and artificial intelligence.
Ziel des Projekts ist die Entwicklung von neuen Werkzeugen zur Simulation von Schwerionenstrahlen in Targets. Wir möchten die Orts- und Energieverteilung aller Primär- und Sekundärteilchen charakterisieren. Dies ist von Interesse in vielen Feldern: Atomphysik (atomare Wechselwirkung, Ioneneinfang), Kernphysik (Untersuchung der Struktur von Atomkernen), Elektronik (Ablagerung von Elementen), Materialwissenschaften (Analyse von Beschädigungen z.B. eines Tokamaks), Biologie (Untersuchung der Toxikologie von Gewebe durch Ionenanalyse). Die Simulation von schweren Ionen ist schwierig aus zwei Gründen: Zum einen ist die gitterbasierte Simulation von Teilchentransport sehr herausfordernd. Zum anderen basieren die Simulationen auf Messungen der Bremsvermögen der Ionen, und müssen daher als unsicher angesehen werden. Daher entwickeln wir ein neues, Entropie-basiertes Diskretisierungsschema, welches eine Sub-Auflösung unterhalb des numerischen Gitters ermöglicht, und daher geeignet für die Simulation von Strahlen ist. Zusätzlich benutzen wir eine ähnliche Methode zur Behandlung von Unsicherheiten in der Teilchenverteilung, die durch die unsicheren Wirkungsquerschnitte bedingt werden. Unsere Methode ist rechenaufwändig, aber hochgradig parallelisierbar, was sie ideal für moderne Rechnerarchitekturen macht.
Development of an innovative measurement system based on P&DGNAA technology for environmental analysis including new evaluation algorithms.
In the long term, the aim is to create added value for researchers by improving the collection, securing, analysis and general availability and searchability of data. As a positive side effect, scientists from Baden-Württemberg will then also be able to assert themselves more easily in research funding decisions by the EU and DFG, because these strongly desire and support the transfer of knowledge even beyond state borders. (Translated with www.DeepL.com)
