Physics-informed renormalisation group flows
Strongly correlated systems offer some of the most intriguing physics challenges such as competing orders or the emergence of dynamical composite degrees of freedom. Often, the resolution of these physics challenges is computationally hard, but can be simplified enormously by a formulation in terms...
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| Main Authors: | , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
October 2025
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| In: |
Annals of physics
Year: 2025, Volume: 481, Pages: 1-35 |
| DOI: | 10.1016/j.aop.2025.170177 |
| Online Access: | Resolving-System, kostenfrei, Volltext: https://doi.org/10.1016/j.aop.2025.170177 Verlag, kostenfrei, Volltext: https://www.sciencedirect.com/science/article/pii/S0003491625002593 
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| Author Notes: | Friederike Ihssen, Jan M. Pawlowski |
| Summary: | Strongly correlated systems offer some of the most intriguing physics challenges such as competing orders or the emergence of dynamical composite degrees of freedom. Often, the resolution of these physics challenges is computationally hard, but can be simplified enormously by a formulation in terms of the dynamical degrees of freedom and within an expansion about the physical ground state. Such a formulation reduces or minimises the computational challenges and facilitates the access to the physics mechanisms at play. The tasks of finding the dynamical degrees of freedom and the physical ground state can be systematically addressed within the functional renormalisation group approach with generalised field transformations. The present work uses this approach to set up physics-informed renormalisation group flows (PIRG flows): Scale-dependent coordinate transformations in field space induce emergent composites, and the respective flows for the effective action generate a large set of target actions, formulated in emergent composite fields. This novel perspective bears a great potential both for conceptual as well as computational applications: PIRG flows allow for a systematic search of dynamical degrees of freedom and the respective ground state that leads to the most rapid convergence of expansion schemes, thus minimising the computational effort. Secondly, the resolution of the remaining computational tasks within a given expansion scheme can be further reduced by optimising the physics content within a given approximation. Thirdly, the maximal variability of PIRG flows can be used to reduce the analytic and numerical effort of solving the flows within a given approximation. |
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| Item Description: | Online veröffentlicht am: 26. August 2025 Gesehen am 18.02.2026 |
| Physical Description: | Online Resource |
| DOI: | 10.1016/j.aop.2025.170177 |