CovNet: Covariance networks for functional data on multidimensional domains

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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 ...

Cyclic peptides are attractive for drug discovery due to their excellent binding properties and the potential to cross cell membranes. However, by far not all cyclic peptides are cell permeable, and measuring or predicting their membrane permeability is no ...

Unmanned vehicles have become indispensable tools in various fields such as agriculture, environmental monitoring, and disaster response. A crucial component of these platforms is sensor fusion. As measurements are inherently noisy, certain variables canno ...

Dynamic aperture is an important concept for the study of non-linear beam dynamics in circular accelerators. It describes the extent of the phase-space region where a particle's motion remains bounded over a given number of turns. Understanding the feature ...

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We propose a comparative study of three different methods aimed at optimizing existing groundwater monitoring networks. Monitoring piezometric heads in subsurface porous formations is crucial at regional scales to properly characterize the relevant subsurf ...

In this thesis we will present two results on global existence for nonlinear dispersive equations with data at or below the scaling regularity. In chapter 1 we take a probabilistic perspective to study the energy-critical nonlinear Schrödinger equation in ...

In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...

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CovNet: Covariance networks for functional data on multidimensional domains

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

Bulk measurement of membrane permeability for random cyclic peptides in living cells to guide drug development

Factor-graph Optimisation with Vehicle Dynamic Models for Mapping & Sensing Applications

Optimizing Dynamic Aperture Studies with Active Learning

Coupling a recurrent neural network to SPAD TCSPC systems for real-time fluorescence lifetime imaging

Machine Learning for Modeling Stock Returns

Data-driven statistical optimization of a groundwater monitoring network

Probabilistic and Deterministic Wellposedness for Low Regularity Dispersive Equations

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Topics in statistical physics of high-dimensional machine learning