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stat.AP2026
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…
stat.AP2025
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…