A cubic stochastic population model with dual fear effects under the Allee effect produces an analytical steady-state probability distribution that exhibits noise-induced transitions and non-monotonic fear-controlled changes between low- and high-density regimes.
Springer, Berlin, Heidelberg ( 1992)
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Itô tracers are continuous Lagrangian particles governed by a stochastic differential equation whose moments match the numerical advection and diffusion of gas in grid-based hydro simulations.
A machine learning model called neural quantum propagator is introduced to efficiently solve non-Markovian quantum dynamics described by HEOM and applied to simulate spectra of the FMO complex.
New splitting-scheme-based pseudo-likelihood estimators for SDEs with Hölder multiplicative noise that achieve strong convergence, state-space preservation, consistency, and asymptotic normality.
A derivative-free ensemble Kalman-Bucy smoother is developed for continuous-time data assimilation that supports Bayesian causal inference and iterative model structure identification with small ensemble sizes under partial observations.
The authors give practical implementations of order-1/2 and order-1 strong schemes for SDDEs with arbitrary fixed delays by combining linear interpolation on a fixed mesh and an augmented variable-step mesh that includes all required delay points.
Introduces a positivity-preserving logarithmic Milstein scheme and Lie-group integrator for stochastic proton dynamics with consistent pathwise sensitivity estimators for regularised dose deposition.
citing papers explorer
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Dual Fear Mechanisms Shaping Stochastic Population Dynamics under the Allee Effect
A cubic stochastic population model with dual fear effects under the Allee effect produces an analytical steady-state probability distribution that exhibits noise-induced transitions and non-monotonic fear-controlled changes between low- and high-density regimes.
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It\^o tracers: continuous-trajectory Lagrangian particles for Eulerian hydrodynamics
Itô tracers are continuous Lagrangian particles governed by a stochastic differential equation whose moments match the numerical advection and diffusion of gas in grid-based hydro simulations.
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Non-markovian neural quantum propagator and its application to the simulation of ultrafast nonlinear spectra
A machine learning model called neural quantum propagator is introduced to efficiently solve non-Markovian quantum dynamics described by HEOM and applied to simulate spectra of the FMO complex.
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Splitting schemes and estimators for stochastic differential equations with H\"older multiplicative noise
New splitting-scheme-based pseudo-likelihood estimators for SDEs with Hölder multiplicative noise that achieve strong convergence, state-space preservation, consistency, and asymptotic normality.
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A Continuous-Time Ensemble Kalman-Bucy Smoother for Causal Inference and Model Discovery
A derivative-free ensemble Kalman-Bucy smoother is developed for continuous-time data assimilation that supports Bayesian causal inference and iterative model structure identification with small ensemble sizes under partial observations.
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Implementation of Milstein Schemes for Stochastic Delay-Differential Equations with Arbitrary Fixed Delays
The authors give practical implementations of order-1/2 and order-1 strong schemes for SDDEs with arbitrary fixed delays by combining linear interpolation on a fixed mesh and an augmented variable-step mesh that includes all required delay points.
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Geometry, Energy and Sensitivity in Stochastic Proton Dynamics
Introduces a positivity-preserving logarithmic Milstein scheme and Lie-group integrator for stochastic proton dynamics with consistent pathwise sensitivity estimators for regularised dose deposition.