Stochastic model for mixing interface evolution through three-dimensional fracture networks [0.03%]
三维裂缝网络中的混合界面演化随机模型
Daniel M C Hallack,Diogo Bolster,Jeffrey D Hyman et al.
Daniel M C Hallack et al.
We study effective mixing behavior of solutes in steady flows through three-dimensional random fracture networks and find that mixing in these systems is characterized by phenomena distinct from continuous porous media. Network-scale hetero...
Yating Wang,Enmai Lei,Yu-Han Ma et al.
Yating Wang et al.
Active matter represents a class of nonequilibrium systems that constantly dissipate energy to produce directed motion. Controlling active matter to achieve a target state holds great potential for advancements in synthetic molecular motors...
Inertial and confined dynamics of a constant-speed active particle in three dimensions [0.03%]
三维恒速活性粒子的惰性和限制动力学
Glend Ford B Rodriguez,Marissa T Rangaig,Norodin A Rangaig
Glend Ford B Rodriguez
We study a self-propelled particle moving at a constant speed in three spatial dimensions, where the orientation vector evolves via a rotational Langevin equation with Ornstein-Uhlenbeck-like statistics. This formulation ensures a unit prop...
Karan Singh,Kabilan Thirumurugan,V K Chandrasekar et al.
Karan Singh et al.
We investigate the coevolution of network structure and opinion dynamics by integrating a threshold-based complex contagion model with a target rewiring mechanism. In contrast to previous models that allow all nonadopting nodes to rewire in...
Machine-learning-enhanced collision operator for the lattice Boltzmann method based on invariant networks [0.03%]
基于不变网络的机器学习增强型格子玻尔兹曼法碰撞算子
Mario Christopher Bedrunka,Tobias Horstmann,Ben Picard et al.
Mario Christopher Bedrunka et al.
Integrating machine learning (ML) techniques in established numerical solvers represents a modern approach to enhance computational fluid dynamics simulations. Within the lattice Boltzmann method (LBM), the collision operator serves as an i...
Simulation of spin dephasing in arbitrary susceptibility fields using physics-informed neural networks [0.03%]
基于物理的神经网络在任意磁场中模拟自旋去相位现象
L T Rotkopf,J C Holzschuh,H-P Schlemmer et al.
L T Rotkopf et al.
The signal dynamics in MRI are influenced by the geometry of the dephasing domain, as well as by diffusion and susceptibility effects. Analytical solutions exist only for simple models, and there is a clear need for improved simulation meth...
Ajit Mahata,S Leo Kingston,Subrata Ghosh et al.
Ajit Mahata et al.
Predicting extreme events is a challenging task due to their occasional appearance at irregular time intervals and with sudden large amplitudes. In particular, accurate forecasting of both the amplitude and timing of occurrence is difficult...
Directed transport of multiple deformable particles in time-oscillating potentials [0.03%]
时变势场中多个可变形颗粒的定向输运
Jing-Jing Liao,Wei Lin,Jia-Jian Li et al.
Jing-Jing Liao et al.
We numerically investigate the transport behavior of multiple deformable particles in time-oscillating potentials. For a fixed potential asymmetry, the transport direction is determined by the competition between two nonequilibrium driving ...
C J Bradly,N R Beaton,A L Owczarek
C J Bradly
We consider the phase transition induced by compressing a self-avoiding walk in a slab where the walk is attached to both walls of the slab in two and three dimensions, and the resulting phase once the polymer is compressed. The process of ...
Nonideal stability analysis of differentially rotating plasmas with global curvature effects [0.03%]
具有整体曲率效应的差异旋转等离子体的非理想稳定性分析
Alexander Haywood,Fatima Ebrahimi
Alexander Haywood
The linear stability of global nonaxisymmetric modes in differentially rotating, magnetized, nonideal plasma is critical to classifying turbulence and transport phenomena. We investigate the competition between the local magneto-rotational ...