Coupling Model Reduction And Koopman Operator For Faster Than Real-Time Simulation Of Complex Parametrized Dynamical Systems Jobs

CNRS Singapour

Job Brief

We have a vacancy of Coupling Model Reduction And Koopman Operator For Faster Than Real-Time Simulation Of Complex Parametrized Dynamical Systems in our company, CNRS Singapour. This vacancy is based in Singapore. Please go through the job detail mentioned below.

Position Title: Coupling Model Reduction And Koopman Operator For Faster Than Real-Time Simulation Of Complex Parametrized Dynamical Systems
Company: CNRS Singapour
Work Type: Full Time
City of work: Singapore
Salary: Salary detail is not available
URL Expiry: 2022-09-01
Posted on: https://sg.jobsoffices.com

Job Detail

Coupling model reduction and Koopman operator for faster than real-time simulation of complex parametrized dynamical systems

Réf
ABG-106182
Sujet de Thèse

10/06/2022
Financement public/privé

CNRS Singapour

Lieu de travail

Singapour – Singapour

Intitulé du sujet

Coupling model reduction and Koopman operator for faster than real-time simulation of complex parametrized dynamical systems

Champs scientifiques

  • Sciences de l’ingénieur
  • Mathématiques
  • Numérique

Description du sujet

Context and Objectives:

The Singapore-French project Descartes (-2026): Intelligent modelling for DEciSion making in CriticAl uRban sysTEmS started in . In short, this project aims at developing a hybrid AI, combining learning, knowledge and reasoning, which has good properties (need for less resources and data, security, robustness, fairness, ethics) and which aims at being applied on industrial applications of the smart city (digital energy, monitoring of structures, air traffic control).
The project brings together 80 permanent researchers (half from France, half from Singapore), with the support of large industrial groups (Thales, EDF, ESI Group, CETIM Matcor, ARIA…). The areas of research cover many disciplines, including data science, engineering, or human sciences.

Within this project, a large number of PhD students (26), post-doctoral fellows (50), and research engineers (20) will be hired between 2022 and 2024. The research will take place mainly in Singapore, at the premises of CNRS et CREATE.

A specific Work Package (WP) of the project, called “Augmented Hybrid Engineering”, deals with engineering aspects, in terms of data assimilation (hardware, sensors), modelling and simulation, as well as command and control on complex physical systems. Fundamental research is conducted, developing methodologies which are agnostic with respect to any potential application or specific physics, even though specific case studies on systems of the smart city are targeted as proofs of concept.
Hybrid-AI represents a modelling and decision-making framework that combines physics-based first principal models with data-driven AI based residual models to accurately model the underlying system dynamics, and deliver safe and explainable decision-making. One main requirement, for effective application of hybrid AI techniques for diagnosis and prognosis on real-life systems, is to be able to perform faster than real-time computations. This is a challenging task, due to the complexity of the considered systems (strong nonlinearities, multiscale aspects, interactions between system components), and this is the topic of the PhD.

Research program:

In the PhD, we deal with large parametrized dynamical systems that are models describing physical systems (or systems of systems) such as those encountered in smart city. We aim at developing an effective strategy to construct and simulate such complex dynamical systems, for fast predictions. This has to be performed in an offline stage, from all available engineering knowledge (coming from physics-based models and/or from stored sensing data). We wish to address this challenge by merging/coupling reduction techniques (such as POD or PGD) on physics-based models and the data-driven Koopman operator that permits to design and manage complex dynamical systems (without knowing the underlying physics equations). In particular, we plan to build a hybrid twin in which the Koopman operator acts on a correction part of the dynamical system, complementary to the description potentially provided by a given physics-based model. We also plan to tailor the Koopman operator by using physics-based basis functions coming from model reduction.
Quantitative accuracy assessment (certification) of the resulting parametrized hybrid model will be performed, for prognosis and control purposes. Adaptive modelling will also be addressed to compute right at the right cost. The final objective of the PhD is to assess performance and validate the proposed approach on targeted applications of the Descartes programme (digital energy, monitoring of structures, air traffic control).
Strong collaborations with other researchers in the DesCartes project, working on topics of interest (learning from Koopman operator, control synthesis, etc.), will be conducted during the PhD.

Nature du financement

Financement public/privé

Précisions sur le financement

Présentation établissement et labo d’accueil

CNRS Singapour

Working Conditions:

The successful candidate will be enrolled for a 4 years, full-time PhD, hosted at CNRS@CREATE, Singapore, with visits to French institutions. He/she will thus join a highly dynamic international research group, with many opportunities for collaborations.
The PhD, funded through the Descartes project (Franco-Singaporean grant) will start in 2022. The candidate will receive a PhD degree from the National University of Singapore (NUS).

Intitulé du doctorat

Doctorat En sciences de l’ingénieur

Pays d’obtention du doctorat

Singapour

Profil du candidat

Expected profile and skills:

  • Applicants should hold a MsC in Applied Mathematics, Mechanical Engineering, or Computer Science
  • Skills in applied mathematics, physics-based modelling, numerical methods for engineers (simulation-based engineering) are required.
  • Knowledge and strong interest in machine learning and programming (Python, C++) will be appreciated.
  • It is expected that the candidate shows high motivation for the project, as well as good communication (writing, oral) skills for publications in conferences or in scientific journals, and for collaborations with colleagues in the Descartes project.
  • Ability to work in an international environment (with English working language), learning from experienced researchers and transfer knowledge, is also a mandatory requirement.