IMod – Partial differential equations, statistics and data: An interdisciplinary approach to databased modelling
IMod – Partial differential equations, statistics and data:
An interdisciplinary approach to databased modelling
IMod is an interdisciplinary project for building, analysing and testing a framework for databased modelling built on the combination of partial differential equations and statistical modelling, and applied in particular to surface fluid mechanics and neuroscience.
The primary objective of IMod is to develop a novel mathematicalstatistical framework for datadriven models of complex systems, guided by problems in fluid mechanics and neuroscience.
IMod research goals
 Combine partial differential equations and statistical theory to develop models with uncertainty for capturing interactions in complex systems.
 Create effective and fast methods to identify physical parameters from sparsely observed phenomena.
 Rigorously study the systems from a mathematical and physical viewpoint.
 Unite theory and data to develop models in fluid mechanics and neuroscience using tailormade experiments.
Principal investigators
Project partners
Funding
Upcoming activities
 Fall 2023: Startup conference
Publications
Ehrnstrom, Mats; Nik, Katerina; Walker, Christoph. (2022). A direct construction of a full family of Whitham solitary waves. Proceedings of the American Mathematical Society, 151(2).
Ehrnstrom, Mats; Nilsson, Dag; Groves, Mark D. (2022) Existence of Davey–Stewartson type solitary waves for the fully dispersive Kadomtsev–Petviashvilii equation. SIAM Journal on Mathematical Analysis, 54(4).
Ehrnstrom, Mats; Wang, Yuexun. (2022) Enhanced existence time of solutions to evolution equations of Whitham type. Discrete and Continuous Dynamical Systems. Series A, 42(8).
Paige, J., Fuglstad, G.A., Riebler, A., and Wakefield, J. (2022). Spatial Aggregation with Respect to a Population Distribution: Impact on Inference. Spatial Statistics, 52.
Preprints
Zhang, J., Bonas, M., Bolster, D., Fuglstad, G.A., and Castruccio, S. (2023) High Resolution Global Precipitation Downscaling with Latent Gaussian Models and Nonstationary SPDE Structure. arXiv:2302.03148.
Altay, U., Paige, J., Riebler, A., and Fuglstad, G.A. (2022) Jittering Impacts Raster and Distancebased Geostatistical Analyses of DHS Data. arXiv:2211.07442.
Affiliated researchers

Omer Babiker PhD Candidate
+4797370402 omer.babiker@ntnu.no Department of Energy and Process Engineering 
Fredrik Hildrum Postdoctoral researcher
fredrik.hildrum@ntnu.no Department of Mathematical Sciences 
Johanna Ulvedal Marstrander PhD Candidate
johanna.u.marstrander@ntnu.no Department of Mathematical Sciences 
John Paige Postdoctoral Fellow
john.paige@ntnu.no Department of Mathematical Sciences 
Artur Jakub Rutkowski Affiliated
artur.rutkowski@ntnu.no Department of Mathematical Sciences 
Olav Rømcke Postdoctoral Fellow
+47+4741857195 olav.romcke@ntnu.no Department of Energy and Process Engineering 
Douglas Svensson Seth Affiliated
douglas.s.seth@ntnu.no Department of Mathematical Sciences 
Ganesh Vaidya ERCIM Postdoctoral Fellow
ganesh.k.vaidya@ntnu.no Department of Mathematical Sciences 
Kristoffer Varholm Postdoctoral researcher
kristoffer.varholm@ntnu.no Department of Mathematical Sciences 
Stefan Weichert PhD Candidate
stefan.weichert@ntnu.no Department of Energy and Process Engineering 
Jun Xue PhD Candidate
jun.xue@ntnu.no Department of Mathematical Sciences 
Swati Yadav Postdoc fellow
swati.yadav@ntnu.no