H2020Индивидуална стипендия2020–2023

COMPLEX ML · Machine learning and the physics of complex and disordered systems

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

Период: 2020-09-01 → 2023-08-31
Финансиране от ЕС: 260 841 €
Участници: 2
Схема: MSCA-IF

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Накратко на български

Сложните системи, като невронните мрежи в мозъка или социалните взаимодействия, се анализират чрез нови инструменти за компресиране на данни и машинно обучение. Това помага за създаването на по-добри теоретични описания на явления от физиката, биологията и индустриалните енергийни мрежи.

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 and the physics of complex and disordered systems

Physicists aim to derive a description of complex phenomena, often involving astronomical numbers of interacting electrons, atoms, molecules or other constituents in terms of only a handful of relevant quantities. In doing so they, in effect, "compress" the full description of the system into a succinct theory, providing an understanding of the phenomenon and its properties, and possessing predictive power. Surprisingly, a systematic path towards that goal exists, technically known as the Renormalization Group. It is, however, very difficult to perform it in practice, especially for disordered or irregular systems, which are ubiquitous in nature. Examples include quasicrystals, cell assemblies in human brain or interactions between participants of a social network, and form a part of what is collectively referred to as complex systems. Complex systems thus comprise a vast class of phenomena from atomistic and chemical scales, through biology, all the way to social interactions and properties of industrial energy networks. As such their improved understanding is of fundamental importance and benefit to the society. The key objective of the interdisciplinary COMPLEX ML project is to construct new analytical and computational tools helping to develop theoretical descriptions of complex systems. To this end we take the suggestive "compression" metaphor seriously, and, together with collaborators, introduce a new approach to constructing effective theories based on the theory of data compression, originating in computer science. The computational progress is based on close connections with developments in the field of Machine Learning (ML), which over the course of the past ten years have revolutionised many areas of engineering. In the COMPLEX ML project we use such techniques to automate parts of the scientific discovery process. Conversely, we aim to improve to improve certain aspects of ML algorithms themselves, using the established connections to complex systems physics.

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

Цел на проекта

Machine learning (ML) has proven capable of tackling difficult engineering problems in image recognition and automated translation, but even more impressively in domains where traditional algorithmic approaches had struggled, such as game playing. Though the relations between ML and physics are decades old, it only recently attracted a widespread attention of scientists in many subfields of theoretical physics due to its ability to identify patterns in high-dimensional data, and to efficiently approximate complicated functional relationships. At the same time, the empirically oriented philosophy of ML is very different from that of fundamental sciences: a trained model often offers little insights into the qualitatively important aspects of the problem, how the solution was arrived at, what are the guarantees of correctness, and, crucially, how to generalize it. Bridging this conceptual gap is thus of fundamental importance, if ML is to become a powerful and controlled tool in physics research. This interdisciplinary projects aims to bring about successful development and application of ML methods resulting in qualitatively new insights in physics by following a twofold strategy. On the one hand, the performance and training of state-of-the-art ML algorithms will be improved using methods of complex and disordered systems. Specific problems targeted will include novel reinforcement learning schemes, and training of binary neural networks, with input from industrial R&D researchers. On the other, cutting edge ML techniques, particularly those with a strong underpinning in information theory, will be combined with modern computational physics methods to develop new tools for disordered systems. This is motivated by the possibility of using them to study soft materials, providing better understanding of these ubiquitous but complex systems.

Оригинален текст от CORDIS (на английски).

Участници

UNIVERSITAT ZURICH · ZurichКоординаторШвейцария
THE UNIVERSITY OF CHICAGO · CHICAGO ILLINOISСъединени щати