A stabilizing kernel for complex Langevin simulations of real-time gauge theories

Langevin方程数值模拟稳定化的一般隐式分裂

A General Implicit Splitting for Stabilizing Numerical Simulations of Langevin Equations

本文提出了一种用于稳定伊藤随机微分方程蒙特卡罗模拟的一般二阶精度（弱意义）方法。分裂方法包括显式龙格库塔法、半隐式法和梯形法则。我们证明了Oettinger的半隐式方法，并指出它可以推广到任意分裂。

利用各向异性核稳定实时规范理论的复朗之万方法

Stabilizing complex Langevin for real-time gauge theories with an anisotropic kernel

针对复朗之万方法在实时规范研究中现有稳定技术不足的问题，研究修正了复时间围道上的方程离散化，引入各向异性核实现稳定模拟，首次完成实时范围超逆温度的非阿贝尔规范模拟，为QCD实时框架构建提供了新路径。

Kernel controlled real-time Complex Langevin simulation

This study explores the utility of a kernel in complex Langevin simulations of quantum real-time dynamics on the Schwinger-Keldysh contour. We give several examples where we use a systematic scheme to find kernels that restore correct convergence of complex Langevin. The schemes combine prior information we know about the system and the correctness of convergence of complex Langevin to construct a kernel. This allows us to simulate up to $1.5β$ on the real-time Schwinger-Keldysh contour with the 0+1 dimensional anharmonic oscillator using $m=1$, $λ=24$, which was previously unattainable using …

Kernel controlled real-time Complex Langevin simulation

This study explores the utility of a kernel in complex Langevin simulations of quantum real-time dynamics on the Schwinger-Keldysh contour. We give several examples where we use a systematic scheme to find kernels that restore correct convergence of complex Langevin. The schemes combine prior information we know about the system and the correctness of convergence of complex Langevin to construct a kernel. This allows us to simulate up to $1.5β$ on the real-time Schwinger-Keldysh contour with the $0+1$ dimensional anharmonic oscillator using $m=1,λ=24$, which was previously unattainable using t…

带核的复Langevin方程的标量场实时模拟

Real time simulations of scalar fields with kernelled complex Langevin equation

本研究利用带核的复Langevin方程规避符号问题，并通过机器学习优化核以延长实时模拟范围，超越现有最先进水平。

带有核的复杂兰格vin模拟

Complex Langevin simulations with a kernel

本文提出通过核解决复杂兰格vin模拟中的收敛问题，并引入了新的正确性条件及机器学习方法在QCD中的应用。