The generalized Jeffreys-type law is formulated as a multi-term time-fractional Jeffreys-type equation, whose dynamics exhibit rich scaling crossover phenomena entailing different diffusion mechanisms. In this work, we provide a novel physical explanation for the equation from first principles, beginning with a microscopic description based on the continuous-time random walk framework with a generalized waiting time distribution and further deriving the equation from an overdamped Langevin equation subject to a stochastic time-change (subordination). Employing the Laplace transform method, we conduct a rigorous analysis of the equation, establishing its well-posedness and providing a detailed Sobolev regularity analysis. We also develop a novel numerical scheme, termed the CIM-CLG algorithm, which achieves spectral accuracy in both time and space while substantially relaxing the temporal regularity requirements on the solution. The algorithm reduces the computational complexity to $\mathcal{O}(N)$ in time and $\mathcal{O}(M\log M)$ in space and is fully parallelizable. Detailed implementation guidelines and new technical error estimates are provided. Extensive numerical experiments in 1D and 2D settings validate the efficiency, robustness, and accuracy of the proposed method. By integrating stochastic modeling, mathematical analysis, and numerical computation, this work advances the understanding of the generalized Jeffreys-type law and offers a mathematically rigorous and computationally efficient framework for tackling complex nonlocal problems.
翻译:广义Jeffreys型定律被表述为一个多阶时间分数阶Jeffreys型方程,其动力学展现出丰富的标度交叉现象,涉及不同的扩散机制。本文从第一性原理出发,为该方程提供了新颖的物理解释:首先基于具有广义等待时间分布的连续时间随机行走框架给出微观描述,进而从受随机时间变换(从属化)的过阻尼朗之万方程推导出该方程。运用拉普拉斯变换方法,我们对方程进行了严格分析,建立了其适定性,并给出了详细的Sobolev正则性分析。我们还提出了一种新颖的数值格式,称为CIM-CLG算法,该算法在时间和空间上均达到谱精度,同时大幅降低了对解的时间正则性要求。该算法将计算复杂度降至时间$\mathcal{O}(N)$与空间$\mathcal{O}(M\log M)$,且完全可并行化。文中提供了详细的实施指南和新的技术误差估计。在一维和二维设置下的大量数值实验验证了所提方法的效率、鲁棒性和精度。通过整合随机建模、数学分析和数值计算,本研究深化了对广义Jeffreys型定律的理解,并为处理复杂的非局部问题提供了一个数学严谨且计算高效的框架。