7 citations · 9 across the 18 of their papers we have counts for
20 papers
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…
Uncertainty calibration for latent-variable regression models
Zina-Sabrina Duma, Otto Lamminpää, Jouni Susiluoto +6
Uncertainty quantification is essential for scientific analysis, as it allows for the evaluation and interpretation of variability and reliability in complex systems and datasets.…
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…
Statistical parameter identification of mixed-mode patterns from a single experimental snapshot
Alexey Kazarnikov, Robert Scheichl, Irving R. Epstein +2
Parameter identification in pattern formation models from a single experimental snapshot is challenging, as traditional methods often require knowledge of initial conditions or tra…
Cellular Automaton With CNN
Valery Ashu, Zhisong Liu, Heikki Haario +1
Cellular automata (CA) models are widely used to simulate complex systems with emergent behaviors, but identifying hidden parameters that govern their dynamics remains a significan…
Detecting Localized Density Anomalies in Multivariate Data via Coin-Flip Statistics
Sebastian Springer, Andre Scaffidi, Maximilian Autenrieth +4
Detecting localized differences between two samples is a central task in scientific data analysis, required for the identification of signal events, regime changes, or model mismat…
Uncertainty Quantification in Portfolio Temperature Alignment
Hendrik Weichel, Aleksandr Zinovev, Heikki Haario +1
We present a novel Bayesian framework for quantifying uncertainty in portfolio temperature alignment models, leveraging the X-Degree Compatibility (XDC) approach with the scientifi…
Kernel-based retrieval models for hyperspectral image data optimized with Kernel Flows
Zina-Sabrina Duma, Tuomas Sihvonen, Jouni Susiluoto +3
Kernel-based statistical methods are efficient, but their performance depends heavily on the selection of kernel parameters. In literature, the optimization studies on kernel-based…
Surrogate model for Bayesian optimal experimental design in chromatography
Jose Rodrigo Rojo-Garcia, Heikki Haario, Tapio Helin +1
We applied Bayesian Optimal Experimental Design (OED) in the estimation of parameters involved in the Equilibrium Dispersive Model for chromatography with two components with the L…
KF-PLS: Optimizing Kernel Partial Least-Squares (K-PLS) with Kernel Flows
Zina-Sabrina Duma, Jouni Susiluoto, Otto Lamminpää +3
Partial Least-Squares (PLS) Regression is a widely used tool in chemometrics for performing multivariate regression. PLS is a bi-linear method that has a limited capacity of modell…
Parameter estimation for cellular automata
Alexey Kazarnikov, Nadja Ray, Heikki Haario +2
Self-organizing complex systems can be modeled using cellular automaton models. However, the parametrization of these models is crucial and significantly determines the resulting s…
Reconstruction and segmentation from sparse sequential X-ray measurements of wood logs
Sebastian Springer, Aldo Glielmo, Angelina Senchukova +5
In industrial applications, it is common to scan objects on a moving conveyor belt. If slice-wise 2D computed tomography (CT) measurements of the moving object are obtained we call…
A Bayesian Approach to Modelling Biological Pattern Formation with Limited Data
Alexey Kazarnikov, Robert Scheichl, Heikki Haario +1
Pattern formation in biological tissues plays an important role in the development of living organisms. Since the classical work of Alan Turing, a pre-eminent way of modelling has…
Hierarchical Bayesian propulsion power models for marine vessels
Antti Solonen, Ramona Maraia, Sebastian Springer +5
Assessing the magnitude of fuel consumption of marine traffic is a challenging task. The consumption can be reduced by the ways the vessels are operated, to achieve both improved c…
Error control in the numerical posterior distribution in the Bayesian UQ analysis of a semilinear evolution PDE
Maria L. Daza-Torres, J. Cricelio Montesinos-López, Marcos A. Capistrán +2
We elaborate on results obtained in \cite{christen2018} for controlling the numerical posterior error for Bayesian UQ problems, now considering forward maps arising from the soluti…
Resolving Overlapping Convex Objects in Silhouette Images by Concavity Analysis and Gaussian Process
Sahar Zafari, Mariia Murashkina, Tuomas Eerola +3
Segmentation of overlapping convex objects has various applications, for example, in nanoparticles and cell imaging. Often the segmentation method has to rely purely on edges betwe…
Shape recovery from sparse tomographic X-ray data
Heikki Haario, Aki Kallonen, Marko Laine +3
A two-dimensional tomographic problem is studied. The target is assumed to be a homogeneous object bounded by a smooth curve. A Non Uniform Rational Basis Splines (NURBS) curve is…