IMod – Partial differential equations, statistics and data: An interdisciplinary approach to data-based modelling
IMod – Partial differential equations, statistics and data:
An interdisciplinary approach to data-based modelling
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.
IMod research goals
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 tailor-made experiments.
Principal investigators (IMod)
Project manager / PI
Project Partners (IMOD)
University of Oxford
University of Notre Dame
University of Basel
University of Delaware
University of Edinburgh
Norwegian Computing Center
- Fall 2023: Start-up conference
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.
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.
Omer Babiker PhD Candidate+47-97370402 firstname.lastname@example.org Department of Energy and Process Engineering
Fredrik Hildrum Postdoctoral email@example.com Department of Mathematical Sciences
Johanna Ulvedal Marstrander PhD Candidatejohanna.firstname.lastname@example.org Department of Mathematical Sciences
John Paige Postdoctoral Fellowjohn.email@example.com Department of Mathematical Sciences
Artur Jakub Rutkowski Affiliatedartur.firstname.lastname@example.org Department of Mathematical Sciences
Olav Rømcke Postdoctoral Fellow+47-+4741857195 email@example.com Department of Energy and Process Engineering
Douglas Svensson Seth Affiliateddouglas.firstname.lastname@example.org Department of Mathematical Sciences
Ganesh Vaidya ERCIM Postdoctoral Fellowganesh.email@example.com Department of Mathematical Sciences
Kristoffer Varholm Postdoctoral firstname.lastname@example.org Department of Mathematical Sciences
Stefan Weichert PhD Candidatestefan.email@example.com Department of Energy and Process Engineering
Jun Xue PhD Candidatejun.firstname.lastname@example.org Department of Mathematical Sciences
Swati Yadav Postdoc email@example.com
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