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Variational Data Assimilation & Model Learning- I5MMAD11

Applied Mathematics
Coming soon
3 ECTS

MODULE TYPE: SDG11 

MODULE DESCRIPTION

Inverse modelling; fusing datasets with physical models; calibrating PDE models; Learning model terms from large datasets

LEARNING OUTCOMES

At the end, the students are supposed to be able : - To set up a data assimilation inverse formulation e.g. in (geophysical) fluid mechanics, structural mechanics, biology etc in presence of databases, measurements. - To compute efficiently the gradient with a large number of parameters by deriving the adjoint model. - To design the complete optimisation process, - To perform local sensitivities analysis, to calibrate the model, to estimate uncertain parameters by assimilating the data into the physical-based model (PDE but also potentially SDE). - To learn model terms (ODE or PDE) from datasets.

Module level: Advanced

Degree level: Masters

Language: English

Study mode: Online Possible

SDG11 Theme: 

ECTS: 3

Semester: Autumn

Starting date: October, 2021

Finishing date: January, 2022

Enrollment period: 

Participation form:  Please fill this document and send it to the admission-contact person: Marie Agnès Detourbe, detourbe@insa-toulouse.fr

University

INSA

Participation info