6 papers
Identifiability and amortized inference limitations in Kuramoto models
Emma Hannula, Jana de Wiljes, Matthew T. Moores +2
Bayesian inference is a powerful tool for parameter estimation and uncertainty quantification in dynamical systems. However, for nonlinear oscillator networks such as Kuramoto mode…
Inferring Relative Consequences of Mechanical Ventilation from Observational Data Using Game-Based Comparisons
David J. Albers, Tell D. Bennett, Jana de Wiljes +4
Identifying the effects of mechanical ventilation (MV) protocols in critical care requires analyzing data from heterogeneous patient-ventilator systems in the clinical decision-mak…
Partially stochastic deep learning with uncertainty quantification for model predictive heating control
Emma Hannula, Arttu Häkkinen, Antti Solonen +3
Making the control of building heating systems more energy efficient is crucial for reducing global energy consumption and greenhouse gas emissions. Traditional rule-based control…
Filtering with Randomised Observations: Sequential Learning of Relevant Subspace Properties and Accuracy Analysis
Nazanin Abedini, Jana de Wiljes, Svetlana Dubinkina
State estimation that combines observational data with mathematical models is central to many applications and is commonly addressed through filtering methods, such as ensemble Kal…
Data-Driven Performance Measures using Global Properties of Attractors for Black-Box Surrogate Models of Chaotic Systems
Luci Fumagalli, Kathy Lüdge, Jana de Wiljes +2
In climate systems, physiological models, optics, and many more, surrogate models are developed to reconstruct chaotic dynamical systems. We introduce four data-driven measures usi…
Adaptive tempering schedules with approximative intermediate measures for filtering problems
Iris Rammelmüller, Gottfried Hastermann, Jana de Wiljes
Data assimilation algorithms integrate prior information from numerical model simulations with observed data. Ensemble-based filters, regarded as state-of-the-art, are widely emplo…