J
Jari P. Kaipio
Researcher at University of Auckland
Publications - 272
Citations - 11543
Jari P. Kaipio is an academic researcher from University of Auckland. The author has contributed to research in topics: Inverse problem & Electrical impedance tomography. The author has an hindex of 49, co-authored 270 publications receiving 10666 citations. Previous affiliations of Jari P. Kaipio include GE Healthcare & University of Eastern Finland.
Papers
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Journal ArticleDOI
Finite element approximation of the Fokker–Planck equation for diffuse optical tomography
Ossi Lehtikangas,Tanja Tarvainen,Tanja Tarvainen,Ville Kolehmainen,Aki Pulkkinen,Aki Pulkkinen,Simon R. Arridge,Jari P. Kaipio,Jari P. Kaipio +8 more
TL;DR: In this paper, a numerical solution of the Fokker-planck equation using finite element method is developed and validated against Monte Carlo simulation and compared with the diffusion approximation on the boundary of a homogeneous medium and in turbid medium containing a low-scattering region.
Parallelized uwvf method for 3d helmholtz problems
TL;DR: In this paper, the authors investigate the parallelized ultra weak variational formulation (UWVF) method for large-scale 3D Helmholtz problems and propose a method to partition the problem in a balanced way and examine the scalability of the parallel UWVF method.
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Detection of contact failures with the Markov chain Monte Carlo method by using integral transformed measurements
Luiz A. S. Abreu,Luiz A. S. Abreu,Helcio R. B. Orlande,Marcelo J. Colaço,Jari P. Kaipio,Jari P. Kaipio,Ville Kolehmainen,César C. Pacheco,Renato M. Cotta +8 more
TL;DR: In this article, a Bayesian formulation of the inverse heat conduction problem is proposed to detect contact failures in layered composites through the estimation of the contact conductance between the layers.
Journal ArticleDOI
Approximate marginalization of absorption and scattering in fluorescence diffuse optical tomography
Meghdoot Mozumder,Tanja Tarvainen,Simon R. Arridge,Jari P. Kaipio,Cosimo D'Andrea,Ville Kolehmainen +5 more
TL;DR: In this paper, the authors employ the recently proposed Bayesian approximation error approach to fDOT for compensating for the inaccurately known optical properties of the target in combination with the normalized Born approximation model.
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Predicting functional properties of milk powder based on manufacturing data in an industrial-scale powder plant
TL;DR: In this article, a data-driven approach to relate the routinely measured plant conditions to one vital function property known as sediment in an industrial-scale powder plant was examined, and linear regression models based on a chosen set of processing variables were used to predict the sediment values.