15 papers
An unsupervised kernel norm monitoring for fault detection in a time series photovoltaic system
Victoria Jorry, Zina-Sabrina Duma, Satu-Pia Reinikainen +2
Grid-connected photovoltaic systems (GCPVS) are generally robust but remain susceptible to faults that can compromise energy conversion efficiency or raise safety concerns. Promptl…
Efficient Amortized Bayesian Inference for Markov Random Fields via Gradient-Informed Grid Selection
Laura Bazahica, Alejandra Avalos-Pacheco, Matthew Moores +1
Bayesian inference for models with intractable likelihoods, such as Markov random fields, poses a fundamental computational challenge due to the tradeoff between inferential accura…
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…
Probabilistic multivariate statistical process control via kernel parameter uncertainty propagation
Zina-Sabrina Duma, Victoria Jorry, Ayesha Safraz +4
Kernel-based multivariate statistical process control (K-MSPC) extends classical monitoring to nonlinear industrial processes. Its performance depends critically on kernel paramete…
Investigating the Electronic and Magnetic Properties of NaFeMnO Cathode Materials with X-ray Compton Scattering
Veenavee Nipunika Kothalawala, Kosuke Suzuki, Johannes Nokelainen +21
We discuss electronic and magnetic properties of NaFeMnO, a promising Na-ion battery cathode material. Using x-ray Compton scattering, SQUID magnetometry, a…
Inhomogeneous Priors for Bayesian Inverse Problems
Babak Maboudi Afkham, Tomas Soto, Mirza Karamehmedovic +1
Many inverse problems arising in engineering and applied sciences involve unknown quantities with pronounced spatial inhomogeneity, such as localized defects or spatially varying m…