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Source: paper_references, paper_reference_links, observed 2026-08-02T17:48:30.792529Z
Paper Citation Record · LEDGER
As of 15 August 2026, this Paper Citation Record lists 83 of 83 outbound references and 1 inbound Pith citation observation for arXiv:2603.21151.
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Source: paper_references, paper_reference_links, observed 2026-08-02T17:48:30.792529Z
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Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-07-10T06:20:49.175000Z
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83 of 83 outbound references displayed
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Observation 4f10e439-5a68-45bf-b50d-d46612181008 · outbound
Solving Functional Renormalization Group Equations with Neural Networks We find that the training results are largely insensitive to the choice of learning rate within a reason- able range
Reference 1
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Solving Functional Renormalization Group Equations with Neural Networks 4 presents the field derivative of the rescaled effective potential ˆV ′ k(ˆρ) for theO(4) model atT= 100 MeV, which lies below the critical temperatureT c ≈ 150 MeV
Reference 2
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Solving Functional Renormalization Group Equations with Neural Networks Unresolved cited work
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Solving Functional Renormalization Group Equations with Neural Networks (A2), is a differential algebraic equa- tion of index 1
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Solving Functional Renormalization Group Equations with Neural Networks Wetterich, Exact evolution equation for the effective potential, Phys
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Solving Functional Renormalization Group Equations with Neural Networks Unresolved cited work
Reference 13
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Solving Functional Renormalization Group Equations with Neural Networks Berges, N
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Solving Functional Renormalization Group Equations with Neural Networks Canet and H
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Solving Functional Renormalization Group Equations with Neural Networks General framework of the non-perturbative renormalization group for non-equilibrium steady states
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Solving Functional Renormalization Group Equations with Neural Networks Dynamic universality class of Model C from the functional renormalization group
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Solving Functional Renormalization Group Equations with Neural Networks Time-evolution of fluctuations as signal of the phase transition dynamics in a QCD-assisted transport approach
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Solving Functional Renormalization Group Equations with Neural Networks Critical dynamics in a real-time formulation of the functional renormalization group
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Solving Functional Renormalization Group Equations with Neural Networks Universality of pseudo-Goldstone damping near critical points
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Solving Functional Renormalization Group Equations with Neural Networks Critical dynamics of Model H within the real-time fRG approach
Reference 21
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Solving Functional Renormalization Group Equations with Neural Networks Dynamic critical behavior of the chiral phase transition from the real-time functional renormalization group
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Solving Functional Renormalization Group Equations with Neural Networks Universal critical dynamics near the chiral phase transition and the QCD critical point
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Solving Functional Renormalization Group Equations with Neural Networks From Quarks and Gluons to Hadrons: Chiral Symmetry Breaking in Dynamical QCD
Reference 24
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Solving Functional Renormalization Group Equations with Neural Networks The Vacuum Structure of Vector Mesons in QCD
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Solving Functional Renormalization Group Equations with Neural Networks The QCD phase structure at finite temperature and density
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Solving Functional Renormalization Group Equations with Neural Networks Chiral Susceptibility in (2+1)-flavour QCD
Reference 31
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Solving Functional Renormalization Group Equations with Neural Networks Towards quantitative precision in functional QCD I
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Reference 33
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Solving Functional Renormalization Group Equations with Neural Networks Unresolved cited work
Reference 34
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Solving Functional Renormalization Group Equations with Neural Networks Unresolved cited work
Reference 35
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Solving Functional Renormalization Group Equations with Neural Networks Fermion Interactions and Universal Behavior in Strongly Interacting Theories
Reference 36
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Solving Functional Renormalization Group Equations with Neural Networks The nonperturbative functional renormalization group and its applications
Reference 37
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Solving Functional Renormalization Group Equations with Neural Networks QCD at finite temperature and density within the fRG approach: An overview
Reference 38
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Solving Functional Renormalization Group Equations with Neural Networks Machine Learning in Nuclear Physics
Reference 39
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Solving Functional Renormalization Group Equations with Neural Networks Exploring QCD matter in extreme conditions with Machine Learning
Reference 40
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Solving Functional Renormalization Group Equations with Neural Networks Physics-Driven Learning for Inverse Problems in Quantum Chromodynamics
Reference 41
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Observation a08f22c7-bcb5-4b1f-80f7-9ee849294518 · outbound
Solving Functional Renormalization Group Equations with Neural Networks Applications of Machine Learning to Lattice Quantum Field Theory
Reference 42
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Solving Functional Renormalization Group Equations with Neural Networks Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics
Reference 43
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Solving Functional Renormalization Group Equations with Neural Networks Diffusion Models as Stochastic Quantization in Lattice Field Theory
Reference 44
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Reference 46
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Solving Functional Renormalization Group Equations with Neural Networks Unresolved cited work
Reference 47
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Solving Functional Renormalization Group Equations with Neural Networks Aarts, D
Reference 48
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Solving Functional Renormalization Group Equations with Neural Networks Raissi, P
Reference 49
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Reference 50
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Reference 51
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Reference 55
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