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PhD in Applied Mathematics: Active learning with functional inputs: application to wind turbine reliability design

PhD in Applied Mathematics: Active learning with functional inputs: application to wind turbine reliability design

France 31 Aug 2024
IFP Energies nouvelles IFPEN

IFP Energies nouvelles IFPEN

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OPPORTUNITY DETAILS

State University
Area
Host Country
Deadline
31 Aug 2024
Study level
Opportunity type
PhD
Specialities
Opportunity funding
Full funding
Eligible Countries
This opportunity is destined for all countries
Eligible Region
All Regions

5 Feb 2024
Job Information
Organisation/Company

IFP Energies nouvelles (IFPEN)
Department

Applied Mathematics
Research Field

Mathematics » Statistics
Researcher Profile

First Stage Researcher (R1)
Country

France
Application Deadline

31 Aug 2024 - 23:59 (Europe/Paris)
Type of Contract

Temporary
Job Status

Full-time
Hours Per Week

35
Offer Starting Date

4 Nov 2024
Is the job funded through the EU Research Framework Programme?

Not funded by an EU programme
Is the Job related to staff position within a Research Infrastructure?

No

Offer Description

Many applications at IFPEN rely on computationally expensive simulators which take scalar variables as inputs, but also functional variables representing, for example, the geometry of mechanical pieces, or spatio-temporal environmental conditions (such as wind). In this context of costly simulators, it is often necessary to use a surrogate model to evaluate efficiently the output of interest for a large number of input parameter values. This substitution model is generally built adaptively by an active learning methodology, from simulations associated with an initial design of limited size. This design is then enriched using criteria adapted to the operational objective, such as optimization of the quantities of interest or estimation of the set of feasible parameters. In the presence of functional variables in the simulator inputs, meta-modeling and experimental design approaches need to be adapted. Conventional approaches are based on dimension reduction or feature extraction methods, the functional variables being represented in the reduced space thus defined. This preliminary step of dimension reduction necessarily induces a loss of information that needs to be quantified and even controlled during the procedure.

The aim of this thesis is to develop active learning approaches for the construction of a substitution model taking functional and scalar variables as inputs, working directly in the functional space of the inputs, and therefore without preliminary dimension reduction. The methods developed will be evaluated on several applications of shape optimization and for the estimation of feasible domains in wind turbine design and CO2 capture processes.

Keywords: Active learning, uncertainties, optimization, design of experiments

Academic supervisors: Clémentine PRIEUR and Céline HELBERT

Doctoral School: ED 217 MSTII, Université Grenoble Alples


Requirements
Research Field
Mathematics » Statistics
Education Level
Master Degree or equivalent

Skills/Qualifications

Master's degree in Statistics or Machine Learning or Optimization


Specific Requirements

Programming languages: R and/or Python


Languages
ENGLISH
Level
Excellent

Additional Information
Benefits

IFP Energies nouvelles is a French public-sector research, innovation and training center. Its mission is to develop efficient, economical, clean and sustainable technologies in the fields of energy, transport and the environment. For more information, see our WEB site .

IFPEN offers a stimulating research environment, with access to first in class laboratory infrastructures and computing facilities. IFPEN offers competitive salary and benefits packages. All PhD students have access to dedicated seminars and training sessions.


Work Location(s)
Number of offers available
1
Company/Institute
Laboratoire Jean Kuntzmann
Country
France
City
Saint Martin d’Hères
Postal Code
38400
Street
150 place du Torrent

Number of offers available
1
Company/Institute
IFP Energies nouvelles
Country
France
City
Rueil-Malmaison
Postal Code
92852
Street
1 et 4 avenue de Bois Préau
Geofield


Where to apply
E-mail

[email protected]

Contact
City

Rueil-Malmaison
Website

http://www.ifpenergiesnouvelles.com/
Street

4 avenue de Bois-Préau
Postal Code

92852

STATUS: EXPIRED

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