34 citations · 87 across the 7 of their papers we have counts for
7 papers
Neural-network solutions to stochastic reaction networks
Ying Tang, Jiayu Weng, Pan Zhang
The stochastic reaction network in which chemical species evolve through a set of reactions is widely used to model stochastic processes in physics, chemistry and biology. To chara…
Learning nonequilibrium statistical mechanics and dynamical phase transitions
Ying Tang, Jing Liu, Jiang Zhang +1
Nonequilibrium statistical mechanics exhibit a variety of complex phenomena far from equilibrium. It inherits challenges of equilibrium, including accurately describing the joint d…
Optimizing higher-order network topology for synchronization of coupled phase oscillators
Ying Tang, Dinghua Shi, Linyuan Lü
Networks in nature have complex interactions among agents. One significant phenomenon induced by interactions is synchronization of coupled agents, and the interactive network topo…
Potential landscape of high dimensional nonlinear stochastic dynamics with large noise
Ying Tang, Ruoshi Yuan, Gaowei Wang +2
Quantifying stochastic processes is essential to understand many natural phenomena, particularly in biology, including cell-fate decision in developmental processes as well as gene…
Work Relations Connecting Nonequilibrium Steady States Without Detailed Balance
Ying Tang, Ruoshi Yuan, Jianhong Chen +1
Bridging equilibrium and nonequilibrium statistical physics attracts sustained interest. Hallmarks of nonequilibrium systems include a breakdown of detailed balance, and an absence…
Summing over trajectories of stochastic dynamics with multiplicative noise
Ying Tang, Ruoshi Yuan, Ping Ao
We demonstrate that the conventional path integral formulations generate inconsistent results exemplified by the geometric Brownian motion under the general stochastic interpretati…
Determine dynamical behaviors by the Lyapunov function in competitive Lotka-Volterra systems
Ying Tang, Ruoshi Yuan, Yian Ma
Global dynamical behaviors of the competitive Lotka-Volterra system even in 3-dimension are not fully understood. The Lyapunov function can provide us such knowledge once it is con…