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КартаВръзкиТемиБългарияНощта (K-TRIO)Мария Склодовска-КюриЗа ученициЗа учителиМетодологияИзненадай ме
H2020Докторантска мрежа2021–2024

GREYDIENT · European Training Network on Grey-Box Models for Safe and Reliable Intelligent Mobility Systems

„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“

Период
2021-01-01 → 2024-12-31
Финансиране от ЕС
3 938 270 €
Участници
10
Схема
MSCA-ITN

Линиите свързват координатора с партньорите.

Накратко на български

Хибридни модели, съчетаващи физични закони и реални данни, се разработват за по-точно предвиждане на повреди в системи като енергийни мрежи и батерии. Това помага за повишаване на безопасността и намаляване на разходите при проектирането на сложни инженерни системи.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

European Training Network on Grey-Box Models for Safe and Reliable Intelligent Mobility Systems

Every engineered system is designed based on expected conditions and lifespan, yet uncertainties arise due to deviations in real-world operation. These uncertainties can lead to unexpected failures, impacting safety, efficiency, and long-term sustainability. Traditional approaches to managing uncertainty rely on excessive safety margins, which, while increasing reliability, also drive up costs and resource consumption. A more refined approach is uncertainty quantification, which evaluates the probability of failure by modeling variations in design and operational conditions. However, this method encounters two main challenges: the high computational cost of running complex simulations and the limited accuracy of models relying on either purely physical principles or historical data. To address these limitations, the GREYDIENT project develops grey-box modeling, a hybrid approach that combines physics-based (white-box) and data-driven (black-box) models. By integrating the strengths of both, grey-box models enhance predictive accuracy while reducing computational demands. This approach has broad applications in energy grids, transportation, battery management, and manufacturing, enabling more precise reliability assessments, cost-effective design optimizations, and improved system monitoring. Through the collaboration of 10 academic and industrial partners, GREYDIENT supports 15 PhD researchers in pushing the frontiers of uncertainty quantification, predictive modeling, and robust optimization strategies for complex engineering systems.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

The GREYDIENT innovative training network aims at training a next generation of Early Stage Researchers (ESR) to fully sustain the ongoing transition of European personal mobility towards safe and reliable intelligent mobility systems via the recently introduced framework of grey-box modelling approaches. One of the main challenges that we currently face in this context is the integration of the data captured from the plenitude of sensors that are involved in a particular road-traffic scenario, ranging from monitoring car-component loading situations to power network-reliability estimations. The aim is to fully exploit the potential of merging these data with advanced computational models of components and systems that are widely available in industry in order to fully assess the momentarily safety. Grey box models are an answer to this pressing issue, as they are aimed at optimally integrating (black-box) data driven machine learning tools with (white-box) simulation models to greatly surpass the performance of either framework separately. However, the training of professional profiles in Europe who combine knowledge and experience in state-of-the-art data-driven black box and numerical white box approaches with expertise in methods for reliability and safety estimation is scarce. Therefore, GREYDIENT will train its ESR’s in a wide spectrum of fields, including the modelling, propagation and quantification of the relevant variabilities, the application of big data and machine learning methods, as well as the optimal combination of data-driven approaches with numerical models. All our ESR’s will obtain a PhD from an internationally respected University, build experience in communicating and disseminating their work, applying their research skills in a non-academic context and receive in-depth training in transferable skills such commercialization, collaboration and entrepreneurship. This training will be organized in close cooperation with key industry stakeholders.

Оригинален текст от CORDIS (на английски).

Участници

Връзки

Данни: CORDIS, © Европейски съюз