- Published on 22 August 2022
Editors: Roberta Citro and Silvia Scarpetta
This special topic will cover different aspects of machine learning for the description of quantum many-body and physics systems, from both solid state, statistical mechanics and computer science. Recently many successfull applications of machine learning has given novel insights in several domains in physics.
Applications of Machine learning tecniques in physics will be discussed as a complementary method to current computational techniques for many-body systems, including Monte Carlo and tensor networks, as well as methods to analyze "big data" generated in experiments.
Foundational questions in machine learning theory will be addressed, as well as the theoretical connections between deep learning, statistical physics, renormalization group theory and neural-networks. Interactions between statistical physics and machine learning has a long history, and many exciting results are coming up recently in the field of statistical theory of learning.
We expect to build up a volume in which readers can find new directions in the cross-fertilizing fields of machine learning and physics, with applications of recent machine learning methods in performing multifaceted tasks, e.g. to design new experiments. Moreover, the reader will be exposed to a new multidisciplinary field and will enrich his/her vision of computational techniques.
The proposed issue will cover areas including:
- Deep Learning and Fast Machine Learning Techniques for scientific discoveries
- Artificial Intelligence
- Novel quantum Monte Carlo Methods
- Applications of Machine Learning and Artificial Intelligence to sub-atomic physics
- Applications of Bayesian optimization and Bayesian statistics and Bayesian Machine Learning to many-body problems
- Statistical physics of machine learning
The Guest Editors invite authors to submit their original research and short reviews on the theme of the Special Issue of the European Physical Journal - Special Topics. Articles may be one of four types: (i) minireviews (10-15 pages), (ii) tutorial reviews (15+ pages), (iii) original paper v1 (5-10 pages), or (iv) original paper v2 (3-5 pages). More detailed descriptions of each paper type can be found in the Submission Guidelines. Manuscripts should be prepared using the latex template (preferably single column layout), which can be downloaded here.
Articles should be submitted to the Editorial Office of EPJ ST via the submission system, and should be clearly identified as intended for the topical issue “Machine Learning in Quantum Many Body and Physical Systems”.
Open Access: EPJST is a hybrid journal offering Open Access publication via the Open Choice programme and a growing number of Transformative Agreements which enable authors to publish OA at no direct cost (all costs are paid centrally).