Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems

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Accurately estimating model performance poses a significant challenge, particularly in scenarios where the source and target domains follow different data distributions. Most existing performance prediction methods heavily rely on the source data in their ...

Data analyses in particle physics rely on an accurate simulation of particle collisions and a detailed simulation of detector effects to extract physics knowledge from the recorded data. Event generators together with a geant-based simulation of the detect ...

Reflective writing is known as a useful method in learning sciences to improve the metacognitive skills of students. However, students struggle to structure their reflections properly, limiting the possible learning gains. Previous works in educational tec ...

Adiabatic quantum-flux parametron (AQFP) is an energy-efficient superconducting technology. Before physical design can be performed, AQFP technology mapping involves not only mapping logic into supported gate types but also legalizing the circuit to fulfil ...

The dynamical cavity method and its backtracking version provide a powerful approach to studying the properties of dynamical processes on large random graphs. This work extends these methods to hypergraphs, enabling the analysis of interactions involving m ...

Microorganisms are essential for life on Earth, performing key roles in numerous biological processes. Their influence extends across a wide spectrum, from human health and ecological balance to advancements in biotechnology and industrial applications. Th ...

This study investigates the impact of protracted humanitarian crises on urban development in Bol, a city in Chad’s Lake region, with a focus on spatial transformations and planning implications. The purpose is to assess how displacement-driven urbanization ...

vanishing viscosity, networks. This work has received funding from the Alexander von Humboldt-Professorship program, the Transregio 154 Project "Mathematical Modelling, Simulation and Optimization Using the Example of Gas Networks" of the DFG, the grant PI ...

Buildings play a pivotal role in the ongoing worldwide energy transition, accounting for 30% of the global energy consumption. With traditional engineering solutions reaching their limits to tackle such large-scale problems, data-driven methods and Machine ...

A range of behavioral and contextual factors, including eating and drinking behavior, mood, social context, and other daily activities, can significantly impact an individual's quality of life and overall well-being. Therefore, inferring everyday life aspe ...

This paper introduces a novel method for data-driven robust control of nonlinear systems based on the Koopman operator, utilizing Integral Quadratic Constraints (IQCs). The Koopman operator theory facilitates the linear representation of nonlinear system d ...

The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

Image information about the state of a building after an earthquake, which can be collected without endangering the post-earthquake reconnaissance activities, can be used to reduce uncertainties in response predictions for future seismic events. This paper ...

The desire and ability to place AI-enabled applications on the edge has grown significantly in recent years. However, the compute-, area-, and power-constrained nature of edge devices are stressed by the needs of the AI-enabled applications, due to a gener ...

In this thesis we explore the applications of projective geometry, a mathematical theory of the relation between 3D scenes and their 2D images, in modern learning-based computer vision systems. This is an interesting research question which contradicts the ...

Monitoring forests, in particular their response to climate and land use change, requires studying long time scales. While efficient deep learning methods have been developed to process short time series of satellite imagery, leveraging long time series of ...

As large, data-driven artificial intelligence models become ubiquitous, guaranteeing high data quality is imperative for constructing models. Crowdsourcing, community sensing, and data filtering have long been the standard approaches to guaranteeing or imp ...

Orthogonal frequency-division multiplexing (OFDM) is widely used to mitigate inter-symbol interference (ISI) from multipath fading. However, the open nature of wireless OFDM systems makes them vulnerable to jamming attacks. In this context, pilot jamming i ...

Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems

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Source-Free Domain-Invariant Performance Prediction

Reweighting simulated events using machine-learning techniques in the CMS experiment

Metacognition meets AI: Empowering reflective writing with large language models

Deep-Learning Reconstruction for 7T MP2RAGE and SPACE MRI: Improving Image Quality at High Acceleration Factors

Technology Legalization and Optimization for Adiabatic Quantum-Flux Parametron

Dynamical cavity method for hypergraphs and its application to quenches in the k-XOR-SAT problem

Optimality principles for the analysis of biological processes across scales: From enzyme kinetics to microbiome dynamics

Spatiotemporal dynamics and humanitarian crisis in Bol (Chad): Satellite imagery analysis

CONTROL OF HYPERBOLIC AND PARABOLIC EQUATIONS ON NETWORKS AND SINGULAR LIMITS

Fusing Pre-existing Knowledge and Machine Learning for Enhanced Building Thermal Modeling and Control

Generalization and Personalization of Machine Learning for Multimodal Mobile Sensing in Everyday Life

Koopman-based Data-driven Robust Control of Nonlinear Systems Using Integral Quadratic Constraints

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Reducing uncertainties in response predictions of earthquake-damaged masonry buildings using data from image-based inspection

Exploring High-Performance and Energy-Efficient Architectures for Edge AI-Enabled Applications

Incorporating Projective Geometry into Deep Learning

Multi-temporal forest monitoring in the Swiss Alps with knowledge-guided deep learning

Extensions of Peer Prediction Incentive Mechanisms

Adversarial Training for Jamming-Robust Channel Estimation in OFDM Systems

Robust machine learning for neuroscientific inference