Mathematics

Mathematical research is essential for advancing the natural sciences, technology, economics, and society. Mathematical methods and analyses play a vital role in diverse applications, including meteorology and climate research, financial and energy markets, public health, and the foundations of artificial intelligence.

The close integration of theory and practice sparks new and exciting questions that demand innovative mathematical solutions. This dynamic interplay creates promising opportunities for both research and practical application. At KIT, scientists collaborate on interdisciplinary research projects to analyze current problems, develop novel mathematical tools, and drive significant progress in both applied fields and mathematics itself.

The KIT Graduate School Computational and Data Science provides doctoral and master’s students with a broad interdisciplinary program that brings together advanced mathematical research and real-world applications. The Computational and Data Science degree program integrates mathematics and computer science with a field in the natural sciences, engineering, or economics.

Emerging Field

Mathematics in Sciences, Engineering, and Economics

Mathematics in Sciences, Engineering, and Economics (MathSEE) represents interdisciplinary, mathematically driven cutting-edge research. At its core lies a distinctive tandem principle: 

On one hand, advanced methodological expertise in the mathematical sciences is closely interwoven with real-world applications, enabling breakthroughs across a wide spectrum of domains. These include atmospheric and earth sciences, communication technologies, financial and energy markets, as well as public health. On the other hand, the challenges arising in these applications areas spur foundational innovation in mathematics itself. This reciprocal dynamic fosters progress in key mathematical disciplines such as analysis, numerics, statistics, and high-performance computing, ensuring that theoretical development and practical relevance go hand in hand.

MathSEE’s research portfolio reflects this dual ambition encompasses a broad range of forward-looking topics. These include probabilistic forecasting and AI-based methods for modeling and predicting atmospheric and geophysical phenomena, the development of transparent and robust decision-making frameworks in economic systems, and the mathematical analysis and simulation of wave phenomena under realistic scenarios.

Research Highlights

Illustration von Wellen KIT

Fascination of Waves

Waves are everywhere: we see light waves, hear sound waves, our hearts beat because of depolarization waves, and modern communication relies heavily on electromagnetic waves. To understand their behavior is to understand nature itself. That’s why the Collaborative Research Center "Wave Phenomena: Analysis and Numerics" aims to explore wave propagation in real-world scenarios, simulate it with advanced numerical methods, and ultimately control it – by tightly integrating analysis and numerics.

Price Formation in Financial Markets

The research group “Financial Markets and Frictions – An Intermediary Asset Pricing Approach” examines how financial intermediaries—such as banks, insurance companies, and other brokers – shape price formation in capital markets. Their work centers on understanding why risk premia vary substantially over time and across different asset classes. The team explores whether focusing on financial intermediaries, rather than individual households, provides deeper insights into these dynamics.

Starkregen und Hochwasser gefährden Mensch, Natur und Infrastruktur. Insbesondere in kleineren Flusseinzugsgebieten sind Vorwarnzeiten sehr kurz und Vorhersagen äußerst unsicher. Bestehende hydrologische Modelle können dort die schnelle Reaktion auf extreme Wetterbedingungen bisher nicht zuverlässig abbilden. Das Forschungsvorhaben „KI-gestützte Hochwasserprognose für kleine Einzugsgebiete in Deutschland“  entwickelt deshalb KI-basierte Methoden, um in diesen Gebieten erstmals eine deutschlandweit einheitliche Prognose zu ermöglichen und die Vorhersagegenauigkeit bei Extremen zu steigern.