3 citations · 3 across the 3 of their papers we have counts for
3 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…
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…
A Log-Gaussian Cox Process with Sequential Monte Carlo for Line Narrowing in Spectroscopy
Teemu Härkönen, Emma Hannula, Matthew T. Moores +2
We propose a statistical model for narrowing line shapes in spectroscopy that are well approximated as linear combinations of Lorentzian or Voigt functions. We introduce a log-Gaus…