Project description
Machine learning could become a powerful tool in physics research
Machine learning, the study of computer algorithms that improve automatically through experience, has proven capable of solving complex engineering problems in image recognition, automated translation and gaming. It is now also being considered for applications in theoretical physics due to its ability to identify patterns in high-dimensional data and efficiently approximate complicated functional relationships. The aim of the EU-funded COMPLEX ML project is to make this relationship between machine learning and physics research stronger. Researchers will use ideas and methods from the physics of disordered systems to boost the performance and training of state-of-the-art machine learning algorithms. Furthermore, machine learning techniques will be combined with modern computational physics methods to develop new tools for disordered systems.
Objective
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.
Fields of science
Not validated
Not validated
- natural sciencescomputer and information sciencesartificial intelligencecomputer visionimage recognition
- natural sciencescomputer and information sciencesartificial intelligencemachine learningreinforcement learning
- natural sciencesphysical sciencestheoretical physics
- natural sciencescomputer and information sciencesartificial intelligencecomputational intelligence
- humanitiesphilosophy, ethics and religionphilosophy
Programme(s)
Funding Scheme
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinator
8006 Zurich
Switzerland