Discrete generative diffusion models without stochastic differential equations: A tensor network approach [0.03%]
没有随机微分方程的离散生成扩散模型：张量网络方法
Luke Causer,Grant M Rotskoff,Juan P Garrahan
Luke Causer
Diffusion models (DMs) are a class of generative machine learning methods that sample a target distribution by transforming samples of a trivial (often Gaussian) distribution using a learned stochastic differential equation. In standard DMs...
Xueqi Li,Palash Kumar Pal,Youming Lei et al.
Xueqi Li et al.
In recent studies, it has been established that higher-order interactions in coupled oscillators can induce a process from continuous to explosive phase transition. In this study, we identify a phase transition, termed the stepwise explosiv...
J Gonzalez,J Sheil
J Gonzalez
Already some years ago, Langdon [Phys. Rev. Lett. 44, 575 (1980)10.1103/PhysRevLett.44.575] proposed that inverse bremsstrahlung absorption in plasmas drives free electrons into non-Maxwellian distributions. Radiation-hydrodynamic simulatio...
Matej Mosko,Maria Polackova,Roman Krcmar et al.
Matej Mosko et al.
We propose a vertex representation of the tensor network (TN) for classical spin systems on hyperbolic lattices. The tensors form a network of regular p-sided polygons (p>4) with the coordination number 4. The response to multistate spin sy...
Constrained Hamiltonian systems and physics-informed neural networks: Hamilton-Dirac neural networks [0.03%]
约束哈密顿系统和物理信息神经网络：Hamilton-Dirac神经网络
Dimitrios A Kaltsas
Dimitrios A Kaltsas
The effectiveness of physics-informed neural networks (PINNs) for learning the dynamics of constrained Hamiltonian systems is demonstrated using the Dirac theory of constraints for regular systems with holonomic constraints and systems with...
Operational solution for the generalized Fokker-Planck and generalized diffusion-wave equations [0.03%]
广义的福克-普朗克方程和广义扩散-波动方程的操作解法
K Górska
K Górska
The evolution operator method is used to solve the generalized Fokker-Planck equations and the generalized diffusion-wave equations in the (1+1)-dimensional space in which x∈R and t∈R_{+}. These equations contain either the first- or the ...
Multimotor cargo navigation in microtubule networks with various mesh sizes [0.03%]
在具有各种网格尺寸的微管网络中多电机货物导航
Mason Grieb,Nimisha Krishnan,Jennifer L Ross
Mason Grieb
The kinesin superfamily of motor proteins is a major driver of anterograde transport of vesicles and organelles within eukaryotic cells via microtubules. Numerous studies have elucidated the step size, velocities, forces, and navigation abi...
Fast and slow escapes in forced chaotic scattering: The Newtonian and the relativistic regimes [0.03%]
强迫混沌散射的快速和慢速逃逸：牛顿和相对论情况
Juan C Vallejo,Alexandre R Nieto,Jesús M Seoane et al.
Juan C Vallejo et al.
We study chaotic scattering phenomena in the paradigmatic Hénon-Heiles Hamiltonian when a rotating external force is applied. This analysis builds on and extends our previous work on the relativistic unperturbed case and the classical case...
Emergence in kinetic roughening with long-range temporal correlations [0.03%]
具有长时间关联性的动力学粗糙现象中的涌现效应
Shuting Wang,Hui Xia
Shuting Wang
The role of long-range temporal correlations in kinetic roughening processes is significant, potentially influencing surface morphologies and dynamic scaling properties. This type of correlation, acting as a nonlocal interaction during grow...
Spin-S Ising models with multispin interactions on the one-dimensional chain and two-dimensional square lattice [0.03%]
一维链和二维方格晶格上的具有多自旋相互作用的自旋-S伊辛模型
Kohei Suzuki
Kohei Suzuki
We study spin-S Ising models with p-spin interactions on the one-dimensional chain and the two-dimensional square lattice. Here, S denotes the magnitude of the spin and p represents the number of spins involved in each interaction. The anal...