7 citations · 9 across the 18 of their papers we have counts for
4 papers · 1 filter
Evaluation of optimisation and Bayesian inference methods for reaction rates in atmospheric chemical mechanisms
Valery Ashu, Wenqing Peng, Zhi-Song Liu +7
Constraining reaction rate coefficients is a central challenge in the development of explicit atmospheric chemical mechanisms, particularly for autoxidation systems where many reac…
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…
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…
MCMC Informed Neural Emulators for Uncertainty Quantification in Dynamical Systems
Heikki Haario, Zhi-Song Liu, Martin Simon +1
Neural networks are a commonly used approach to replace physical models with computationally cheap surrogates. Parametric uncertainty quantification can be included in training, as…