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KoOpeRaDE

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Koopman Operator based Reinforcement Learning Control of Partial Differential Eq...

Koopman Operator based Reinforcement Learning Control of Partial Differential Equations An unprecedented energy crisis is looming over us. In order to transition to a greener and more energy-efficient society, existing technologies need to be improved and novel techniques such as nuclear fusion developed. This requir... ver más

31/12/2029

TUDO

1M€

Presupuesto del proyecto: 1M€

Líder del proyecto

TECHNISCHE UNIVERSITAT DORTMUND No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2024-09-23

ERC-2024-STG: ERC STARTING GRANTS Scope:Objectives

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Este proyecto no cuenta con búsquedas de partenariado abiertas en este momento.

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1 Participantes

TUDO

1.50M€ | Lider

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Duración del proyecto: 63 meses Fecha Inicio: 2024-09-23
Fecha Fin: 2029-12-31

Líder del proyecto

TECHNISCHE UNIVERSITAT DORTMUND No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 1M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto An unprecedented energy crisis is looming over us. In order to transition to a greener and more energy-efficient society, existing technologies need to be improved and novel techniques such as nuclear fusion developed. This requires the stabilization of aerodynamics, heat transfer or combustion and fusion processes and thus, the development of efficient control strategies for large-scale dynamical systems. In recent years, reinforcement learning (RL) has emerged as a highly promising data-driven technique. Unfortunately, we cannot trust RL to handle our most important and complex systems, since the resulting controllers do not possess performance guarantees. Certifiable RL approaches such as linear or kernel methods tend to scale poorly, such that their applicability is limited to toy examples. In contrast to other application areas, this is a complete show-stopper for safety-critical engineering. Moreover, the training is extremely data hungry and costly, due to which RL itself contributes to the energy crisis. The vision of this project is to develop new foundational methods to equip RL controllers for large-scale engineering systems with performance guarantees by exploiting system knowledge and systematically reducing the complexity. To achieve this, I will target three major breakthroughs, consisting of (A) global linearization of the dynamics via the Koopman operator framework, (B) the extension of certified Q-learning to continuous action spaces via control quantization, and (C) the detection and exploitation of symmetries in the system dynamics. The project requires significant joint advancements in several challenging areas such as control, approximation theory and machine learning. In the case of success, the resulting controllers will provide a massive advancement of RL towards safety-critical engineering applications and significantly contribute to the challenge of meeting the future energy demands of our society.

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