H2020Индивидуална стипендия2015–2017

MALCOD · Machine Learning for Computational Dynamics

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

Период: 2015-09-01 → 2017-08-31
Финансиране от ЕС: 171 461 €
Участници: 1
Схема: MSCA-IF

Линиите свързват координатора с партньорите.

Накратко на български

Методите за машинно обучение се прилагат за анализ на динамични системи, като например турбулентните потоци около крилата на самолет. Това помага за по-точното изчисляване на грешките и поведението на сложни мрежи и процеси.

Този кратък обзор е генериран от изкуствен интелект

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

Резултати накратко

Machine Learning for Computational Dynamics

The project aimed to establish new methods for the numerical analysis of dynamical systems by using tools from the field of machine learning. This interdisciplinary work was largely unexplored, the MALCOD project began a systematic development of this field. The approach was motivated by kernel methods in machine learning, in particular the functional analytic framework provided by the reproducing kernel Hilbert space (RKHS) that underlies many modern machine learning methods. The key objectives for the research outlined in the proposal were: 1. Numerical approximation and continuation for control and random dynamical systems 2. Numerical approximation of Lyapunov functions and basins of attraction for deterministic dynamical systems 3. Error analysis of the numerical methods from 1 and 2 4. Application to problems in power grid networks, movie image rendering and turbulent flow across aerofoils The project work was based at the Potsdam Institute for Climate Impact Research (PIK). Another key component of the project was to have secondments with the non-academic partner organisation Ambrosys GmbH in Potsdam, Germany. The project was terminated early, and ran from 01.09.2015 to 31.03.2016.

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

Цел на проекта

The proposed research aims to establish groundbreaking new methods for the numerical analysis of dynamical systems by using tools from the field of machine learning. The intersection of the fields of machine learning and computational dynamics is largely unexplored, and this proposal aims at the first systematic development of a unified theory, with a view to applying the ideas to problems in the commercial and energy sectors. Recent results by the applicant in set approximation for control systems demonstrate the power of this approach, the results of which significantly improve on the current state-of-the-art methods for set approximation. This approach is based on a functional analytic framework frequently exploited in modern machine learning methods: the reproducing kernel Hilbert space (RKHS). Algorithms are designed to seek functions in the RKHS that characterise important dynamical properties of the system. This highly interdisciplinary research programme will develop a powerful and unified approach to create new algorithms that can either use input data generated from the evolution equations (if they are available) or measured data obtained directly from applications.The host institution PIK is a transdisciplinary host institution focused on climate modeling and sustainability. The tools developed during the course of the fellowship will be applied to the problem of basin stability and synchronisation of power grid networks. This proposal also includes two secondment phases to be spent at the non-academic partner organisation Ambrosys GmbH (AMB). There, the applicant will apply the research results to problems in image rendering in movies and turbulent flow across aerofoils, which are commercial applications already studied at AMB. The applicant will benefit from training in climate modeling and complex systems at PIK, and industrial training during the secondment phases.

Оригинален текст от CORDIS (на английски).

Участници

POTSDAM-INSTITUT FUR KLIMAFOLGENFORSCHUNG EV · PotsdamКоординаторГермания