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КартаВръзкиТемиБългарияНощта (K-TRIO)Мария Склодовска-КюриЗа ученициЗа учителиМетодологияИзненадай ме
HEДокторантска мрежа2023–2027

DATAHYKING · Data-driven simulation, uncertainty quantification and optimization for hyperbolic and kinetic models

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

Период
2023-03-01 → 2027-09-30
Финансиране от ЕС
3 465 346 €
Участници
20
Схема
HORIZON-TMA-MSCA-DN-JD

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Накратко на български

Математическите модели на взаимодействащи частици анализират системи като разпространението на прахови частици във въздуха или движението на автомобили. Те помагат за създаването на по-точни симулации, които съчетават микроскопичните взаимодействия с големите масиви от данни.

Този кратък обзор е генериран от изкуствен интелект

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

Резултати накратко

Data-driven simulation, uncertainty quantification and optimization for hyperbolic and kinetic models

Many systems of interest can be described as a large number of particles that interact in a highly non-intuitive way. In pollution, for example, these particles can be fine dust or aerosol particles, in mobility they are individual vehicles, in financial systems, they are individual banks or even consumers. Mathematical models of interacting particles are often based on hyperbolic or kinetic models, which provide a common mathematical structure. Hyperbolic models give a macroscopic description, in terms of particle density, momentum and energy. Kinetic models describe the system at a more microscopic level and contain detailed information about individual particle interactions. Current challenges force scientists to take into account the precise (microscopic) interactions between individual particles, as these interactions directly influence the behaviour that emerges at the macroscopic scale of interest. At the same time, the availability of massive amounts of measurement data allows the calibration of increasingly complex models. Nevertheless, computer simulation of interacting particle systems is usually done with highly approximate (macroscopic) models to reduce computational complexity. Facing these challenges without sacrificing the complexity of the underlying particle interactions requires a fundamentally new type of scientist that uses an interdisciplinary approach and a solid mathematical underpinning. Hence, in the DATAHYKING Doctoral Network, we aim at training a new generation of modeling and simulation experts to develop virtual experimentation tools and workflows that can reliably and efficiently exploit the potential of mathematical modeling and simulation of interacting particle systems. To this end, we create a data-driven simulation framework for kinetic models of interacting particle systems, and define a common methodology for these future modeling and simulation experts. DATAHYKING will focus on: Developing reliable and efficient simulation methods; Designing robust consensus-based optimisation, also for machine learning; Developing multifidelity methods for uncertainty quantification and data assimilation; Applications in traffic flow, finance and granular flow, also in collaboration with industry. Website: https://www.datahyking.eu

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

Цел на проекта

Europe faces major challenges in science, society and industry, induced by the complexity of our dynamically evolving world. To tackle these challenges, mathematical models and computer simulations are indispensable, for instance to design and optimize systems using virtual prototypes. Moreover, while the big data revolution provides additional possibilities, it is currently unclear how to optimally combine simulation results with observation data into a digital. Many systems of interest consist of large numbers of particles with highly non-trivial interaction (e.g., fine dust in pollution, vehicles in mobility).However, to date, computer simulation of such systems is usually done with highly approximate (macroscopic) models to reduce computational complexity. Facing these challenges without sacrificing the complexity of the underlying particle interactions requires a fundamentally new type of scientist that uses an interdisciplinary approach and a solid mathematical underpinning. Hence, we aim at training a new generation of modeling and simulation experts to develop virtual experimentation tools and workflows that can reliably and efficiently exploit the potential of mathematical modeling and simulation of interacting particle systems. To this end, we create a data-driven simulation framework for kinetic models of interacting particle systems, and define a common methodology for these future modeling and simulation experts. The network focuses on (i) reliable and efficient simulation; (ii) robust consensus-based optimisation, also for machine learning; (iii) multifidelity methodes for uncertainty quantification and Bayesian inference; and (iv) applications in fluid flow, traffic flow, and finance, also in collaboration with industry. Moreover, the proposed EJD program will create a closely connected new generation of highly demanded European scientists, and initiate long-term partnerships to exploit synergy between academic and industrial partners.

Оригинален текст от CORDIS (на английски).

Участници

Връзки

Данни: CORDIS, © Европейски съюз