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Mathematical Physics is a section of Frontiers in Physics which focuses on all areas associated with the mathematical methods applied to physical problems. The section aims at publishing results on all areas of physics with a strong mathematical content. Mathematical Physics welcomes manuscripts with rigorous mathematical formulations so that physical ‘reality’ is described through simple but solid, far reaching, universal models.
Areas covered by this section include, but are not limited to:
· Classical and Quantum Field Theory
· Classical and Quantum Mechanics
· Emergent Theories
· Fractional Calculus
· General Relativity
· Methods in Neural Networks
· Modern Relations between Combinatorics and Physics
· Partial Differential Equations
· Probability Theory
· Quantum Gravity
· Quantum Information
· Statistical Mechanics
· String Theory
Indexed in: Google Scholar, DOAJ, CrossRef, Semantic Scholar, CLOCKSS
Mathematical Physics welcomes submissions of the following article types: Correction, Data Report, Editorial, General Commentary, Hypothesis and Theory, Methods, Mini Review, Opinion, Original Research, Perspective, Review, Specialty Grand Challenge and Technology and Code.
All manuscripts must be submitted directly to the section Mathematical Physics, where they are peer-reviewed by the Associate and Review Editors of the specialty section.
Articles published in the section Mathematical Physics will benefit from the Frontiers impact and tiering system after online publication. Authors of published original research with the highest impact, as judged democratically by the readers, will be invited by the Chief Editor to write a Frontiers Focused Review - a tier-climbing article. This is referred to as "democratic tiering". The author selection is based on article impact analytics of original research published in all Frontiers specialty journals and sections. Focused Reviews are centered on the original discovery, place it into a broader context, and aim to address the wider community across all of Physics and Applied Mathematics and Statistics.
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