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Introduction to data assimilation and dimensionality reduction
01HWWIW
A.A. 2024/25
Course Language
Inglese
Degree programme(s)
Doctorate Research in Ingegneria Aerospaziale - Torino
Course structure
| Teaching | Hours |
|---|---|
| Lezioni | 20 |
Lecturers
| Teacher | Status | SSD | h.Les | h.Ex | h.Lab | h.Tut | Years teaching |
|---|---|---|---|---|---|---|---|
| Magri Luca | Professore Ordinario | IIND-01/F | 20 | 0 | 0 | 0 | 2 |
Co-lectures
Context
| SSD | CFU | Activities | Area context | *** N/A *** | 4 |
|---|
Presentazione degli algoritmi piu' utilizzati in ingegneria, industria, e scienza che sono basati su la riduzione della complessita' dei dati e l'assimilazione di data in modelli per inferenza dello stato fisico, parametri e errori di modello. Il
corso e' un introduzione a modal decomposition e data assimilation. Conoscenza pregressa in modal decomposition e data assimilation non e' richiesta.
This course introduces the most common algorithms deployed in the engineering, science,
and industry for dimensionality reduction and data assimilation to infer physical states, design parameters and model errors. The course is an introductory course to modal decomposition and data assimilation. No prior knowledge in modal decomposition e data assimilation is required.
Laurea magistrale (o equivalente) in ingegneria, e/o matematica, e/o fisica, e/o informatica, e/o equivalente.
MEng/MSc Degree (or equivalent) in engineering, and/or maths, and/or physics, and/or computer science, and/or
equivalent. Students must have taken the course "Fondamenti di scientific machine learning" to attend this course.
Fondamenti di modal decomposition; data assimilation con methodi variazionali, data assimilation con metodi Bayesian (senza gradienti, basati su sampling).
Introduction to modal decomposition; data assimilation with variational methods (gradient-based, adjoint-based), data assimilation with Bayesian methods (gradient-free, sampling based).
Modalità mista
Mixed mode
Test a risposta multipla
Multiple choice test
P.D.2-2 - Maggio
P.D.2-2 - May