Colloque - Evie Nielen : Efficient Greedy Sampling for Model Order Reduction

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Previously titled “Colloque - Helin Gong : AI-Driven Complexity Reduction and Multi-Physics Digital Twins: From Theory to Engineering Implementation in Nuclear Reactors” — renamed by the publisher on Sep 22, 2026

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Recorded23 Jun 2026

Publisherpodcast.college-de-france.fr

Handlerec:20038157

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Yvon MadayChaire Informatique et sciences numériquesCollège de FranceAnnée 2025-2026Colloque : Aspects mathématiques et appliqués des méthodes de réduction de complexité - Helin Gong : AI-Driven Complexity Reduction and Multi-Physics Digital Twins: From Theory to Engineering Implementation in Nuclear ReactorsHelin GongAssociate Professor, Paris Elite Institute of Technology, Shanghai Jiao Tong University, Shanghai, China.Résumé To meet the rigorous demands of Best-Estimate Plus Uncertainty (BEPU) in modern nuclear engineering, it is essential to characterize safety margins and system dynamics with both high fidelity and high efficiency. Building upon foundational complexity reduction methods—such as the Generalized Empirical Interpolation Method (GEIM) and Reduced Basis methods—this talk presents the recent advancements in applying these mathematical tools to real-world nuclear engineering practices.By integrating Model Order Reduction (ROM) with Artificial Intelligence (AI) and Data Assimilation, we have developed a data-enabled, physics-informed digital twin framework. This approach effectively resolves high-dimensional multi-physics coupling problems and allows for ultra-real-time state estimation and parameter identification. Furthermore, the presentation will highlight the engineering implementation of these methodologies, demonstrating how theoretical reduced-order models are deployed into industrial software and platform architectures (e.g., AI-Enhanced Digital Twin Engineering Platform) for the online monitoring and predictive simulation of commercial nuclear reactor cores.

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