4 papers
Functional renormalization group for classical liquids without recourse to hard-core reference systems: A study of three-dimensional Lennard-Jones liquids
Takeru Yokota, Jun Haruyama, Osamu Sugino
In our previous work [Phys. Rev. E 104, 014124 (2021)], we developed a method for analyzing classical liquids using the functional renormalization group (FRG) without relying on a…
Unpolarized prethermal discrete time crystal
Takeru Yokota, Tatsuhiko N. Ikeda
Prethermal discrete time crystals (DTCs) are a novel phase of periodically driven matter that exhibits robust subharmonic oscillations without requiring disorder. However, previous…
Physics-informed Neural Networks for Functional Differential Equations: Cylindrical Approximation and Its Convergence Guarantees
Taiki Miyagawa, Takeru Yokota
We propose the first learning scheme for functional differential equations (FDEs). FDEs play a fundamental role in physics, mathematics, and optimal control. However, the numerical…
Physics-informed neural networks for solving functional renormalization group on a lattice
Takeru Yokota
Addressing high-dimensional partial differential equations to derive effective actions within the functional renormalization group is formidable, especially when considering variou…