1 citations · 2 across the 4 of their papers we have counts for
6 papers
Forced-Term Modeling in the Koopman Framework for Linear Operator Learning in Nonlinear Dynamical Systems
Paolo Climaco, Jochen Garcke, Xenia F. Gerloff
We study the problem of extracting reliable and interpretable linear models from nonlinear dynamical systems with periodic behavior. Dynamic Mode Decomposition (DMD) is a widely us…
An Interpretable Approach to Money Laundering Detection in Transaction Graphs using Pass-Through Templates
Paolo Climaco
Layering is a key stage of the money laundering process in which assets are moved through intermediate entities to obscure their origin. We model this movement as a transaction gra…
Density-Aware Farthest Point Sampling
Paolo Climaco, Jochen Garcke
We focus on training machine learning regression models in scenarios where the availability of labeled training data is limited due to computational constraints or high labeling co…
On minimizing the training set fill distance in machine learning regression
Paolo Climaco, Jochen Garcke
For regression tasks one often leverages large datasets for training predictive machine learning models. However, using large datasets may not be feasible due to computational limi…
On the Interplay of Subset Selection and Informed Graph Neural Networks
Niklas Breustedt, Paolo Climaco, Jochen Garcke +5
Machine learning techniques paired with the availability of massive datasets dramatically enhance our ability to explore the chemical compound space by providing fast and accurate…
Multi-resolution Dynamic Mode Decomposition for Damage Detection in Wind Turbine Gearboxes
Paolo Climaco, Jochen Garcke, Rodrigo Iza-Teran
We introduce an approach for damage detection in gearboxes based on the analysis of sensor data with the multi-resolution dynamic mode decomposition (mrDMD). The application focus…