The study of time series is crucial for understanding trends and anomalies over time,
enabling predictive insights across various sectors. Spatio-temporal data, on the other hand …

Here, we present HelixDiff, a score-based diffusion model for generating all-atom helical
structures. We developed a hot spot-specific generation algorithm for the conditional design …

The well-known stochastic Lotka–Volterra model for interacting multi-species in ecology has
some typical features: highly nonlinear, positive solution and multi-dimensional. The known …

Influenced by Higham et al.(2002), several numerical methods have been developed to
study the strong convergence of the numerical solutions to stochastic differential equations …

Solving stochastic differential equations (SDEs) numerically, explicit Euler–Maruyama (EM)
schemes are used most frequently under global Lipschitz conditions for both drift and …

This work develops a novel approximation for a class of superlinear stochastic Kolmogorov
equations with positive global solutions. On the one hand, most existing explicit methods …

Stochastic differential equations (SDEs) are mathematical models that are widely used to
describe complex processes or phenomena perturbed by random noise from different …

Reinforcement learning (RL) is a data‐driven approach to synthesizing an optimal control
policy. A barrier to wide implementation of RL‐based controllers is its data‐hungry nature …

This paper proposes an adaptive timestep construction for an Euler–Maruyama
approximation of SDEs with nonglobally Lipschitz drift. It is proved that if the timestep is …

In this paper, we introduce fully implementable, adaptive Euler–Maruyama schemes for
McKean–Vlasov stochastic differential equations (SDEs) assuming only a standard …