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IMod – Partial differential equations, statistics and data:
An interdisciplinary approach to data-based modelling

Schematic description of project. Chalk on blackboard.

IMod main content


IMod is an interdisciplinary project for building, analysing and testing a framework for data-based 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 mathematical-statistical framework for data-driven models of complex systems, guided by problems in fluid mechanics and neuroscience.

Scientific description.

IMod research goals

IMod research goals

  1. Combine partial differential equations and statistical theory to develop models with uncertainty for capturing interactions in complex systems.
  2. Create effective and fast methods to identify physical parameters from sparsely observed phenomena.
  3. Rigorously study the systems from a mathematical and physical viewpoint.
  4. Unite theory and data to develop models in fluid mechanics and neuroscience using tailor-made experiments.

Principal investigators (IMod)

Project Partners (IMOD)

Project partners

José Antonio Carrillo
University of Oxford
Stefano Castruccio
Associate Professor
University of Notre Dame
Flavio Donato
University of Basel
James T. Kirby
University of Delaware
Finn Lindgren
University of Edinburgh
Thordis Thorarinsdottir
Research Director
Norwegian Computing Center

Funding (IMod)

Activities (IMod)

Upcoming activities

  • Fall 2023: Start-up conference

Publications (IMod)

Preprints (IMod)


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 Distance-based Geostatistical Analyses of DHS Data. arXiv:2211.07442.

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