Developing a coherent mathematical and algorithmic framework optimally combining the strengths of complex physics-based models with (often vast) data sets.
In many fields of science and engineering, decisions are based on the outcomes of models that estimate/predict the state of a physical system or some of its relevant properties. One can distinguish two main families of such predictive models:
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Contact
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Visiting address
Department of Mathematics and Computer ScienceGroene Loper 55612 AP EindhovenNetherlands -
Postal address
MetaforumP.O. Box 5135600 MB EindhovenNetherlands -
Secretarycasa@ tue.nl
Recent Publications
Our most recent peer reviewed publications
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Analysis of (sub-)Riemannian PDE-G-CNNs
Journal of Mathematical Imaging and Vision (2023) -
Inverse Problems
(2023) -
Nonlinear approximation spaces for inverse problems
Analysis and Applications (2023) -
Nonlinear approximation spaces for inverse problems
Analysis and Applications (2023) -
Epidemiological Forecasting with Model Reduction of Compartmental Models
Biology (2021)