Results 171 to 180 of about 238,303 (293)
Numerical computation of the stochastic hepatitis B model using feed forward neural network and real data. [PDF]
Khan T, Jung IH.
europepmc +1 more source
From Stability to Chaos: A Complete Classification of the Damped Klein‐Gordon Dynamics
ABSTRACT We investigate the transition between stability and chaos in the damped Klein‐Gordon equation, a fundamental model for wave propagation and energy dissipation. Using semigroup methods and spectral criteria, we derive explicit thresholds that determine when the system exhibits asymptotic stability and when it displays strong chaotic dynamics ...
Carlos Lizama +2 more
wiley +1 more source
Macroscopic Thermalization for Highly Degenerate Hamiltonians After Slight Perturbation. [PDF]
Roos B +4 more
europepmc +1 more source
Predefined‐Time Dynamic Surface Control of Tank Horizontal Stabilization System With Disturbance
ABSTRACT Modern new tanks are difficult to achieve high precision control of the horizontal stabilization system due to electromechanical coupling and road surface disturbance during high‐speed combat. A predefined‐time dynamic surface controller (DSCPT) is proposed for the fast stabilization and strong disturbance problems of the tank horizontal ...
Zhicheng Fan +5 more
wiley +1 more source
Notes on the Jellinek-Berry Thermostated Ideal Gas. [PDF]
Butler LT, Sharifi A.
europepmc +1 more source
ABSTRACT This study addresses the trajectory tracking control for manipulator robots with uncertainties. The main objective is to ensure that the robot follows a desired trajectory despite the presence of uncertainties/disturbances and considering control input constraints. The approach is developed in the framework of continuous integral sliding modes.
Emanuel Ortiz‐Ortiz +3 more
wiley +1 more source
Population-based variance-reduced evolution over stochastic landscapes. [PDF]
Pei Z +5 more
europepmc +1 more source
Personalized Differential Privacy for Ridge Regression Under Output Perturbation
ABSTRACT The increased application of machine learning (ML) in sensitive domains requires protecting the training data through privacy frameworks, such as differential privacy (DP). Traditional DP enforces a uniform privacy level ε$$ \varepsilon $$, which bounds the maximum privacy loss that each data point in the dataset is allowed to incur.
Krishna Acharya +3 more
wiley +1 more source
A note on spontaneous symmetry breaking in the mean-field Bose gas. [PDF]
Deuchert A, Nam PT, Napiórkowski M.
europepmc +1 more source

