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
del Teso, F., Endal, J., Jakobsen, E.R., and Vazquez, J.L. (2023) Evolution Driven by the Infinity Fractional Laplacian. Calc. Var., 62(136).
del Teso, F., Endal, J., and Jakobsen, E.R. (2023) Uniform tail estimates and Lpconvergence for finitedifference approximations of nonlinear diffusion equations. Discrete Contin. Dyn. Syst., 43(3&4): 1319–1346.
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. Journal of the Royal Statistical Society: Series C. In press.
Lenain, Luc; Smeltzer, Benjamin Keeler; Pizzo, Nick; Maria, Freilich; Colosi, Luke; Ellingsen, Simen Andreas Ådnøy; Grare, Laurent; Peyriere, Hugo; Statom, Nick. (2023) Airborne Remote Sensing of UpperOcean and Surface Properties, Currents and Their Gradients From Meso to Submesoscales. Geophysical Research Letters, 50.
Pizzo, Nick; Lenain, Luc; Rømcke, Olav; Ellingsen, Simen Andreas Ådnøy; Smeltzer, Benjamin Keeler. (2023) The role of Lagrangian drift in the geometry, kinematics and dynamics of surface waves. Journal of Fluid Mechanics, 954.
Smeltzer, Benjamin Keeler; Rømcke, Olav; Hearst, R. Jason; Ellingsen, Simen Andreas Ådnøy. (2023) Experimental study of the mutual interactions between waves and tailored turbulence. Journal of Fluid Mechanics, 962.
Zheng, Zibo; Li, Yan; Ellingsen, Simen Andreas Ådnøy. (2023) Statistics of weakly nonlinear waves on currents with strong vertical shear. Physical Review Fluids, 8.
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; 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
Altay, U., Paige, J., Riebler, A., and Fuglstad, G.A.. (2023) GeoAdjust: Adjusting for Positional Uncertainty in Geostatistial Analysis of DHS Data. arXiv:2303.12668.
Altay, U., Paige, J., Riebler, A., and Fuglstad, G.A. (2022) Jittering Impacts Raster and Distancebased Geostatistical Analyses of DHS Data. arXiv:2211.07442.
Junior researchers

Jørgen Røysland Aarnes Postdoctoral Fellow
jorgen.r.aarnes@ntnu.no Department of Energy and Process Engineering 
Øyvind Auestad PhD Candidate
oyvinau@ntnu.no Department of Mathematical Sciences 
Daniel Kjellevold PhD Candidate
daniel.kjellevold@ntnu.no Department of Mathematical Sciences 
Robin Østern Lien PhD Candidate
+4741179069 robin.o.lien@ntnu.no Department of Mathematical Sciences 
Douglas Svensson Seth Postdoctoral Fellow
douglas.s.seth@ntnu.no Department of Mathematical Sciences
Affiliated researchers

Omer Babiker PhD Candidate
+4797370402 omer.babiker@ntnu.no Department of Energy and Process Engineering 
Indranil Chowdhury Assistant Professor, Department of Mathematics and Statistics, IIT KANPUR

Simen Knutsen Furset PhD Candidate
simen.k.furset@ntnu.no Department of Mathematical Sciences 
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 Associate Professor
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 Postdoctoral Fellow
douglas.s.seth@ntnu.no Department of Mathematical Sciences 
Ganesh Vaidya ERCIM Postdoctoral Fellow
ganesh.k.vaidya@ntnu.no 
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
jun.xue@ntnu.no 
Swati Yadav Postdoc fellow
swati.yadav@ntnu.no