Results 1 to 10 of about 116,653,606 (265)

A non-standard computational method for stochastic anthrax epidemic model

AIP Advances, 2023
This study employing a non-standard computational method for a stochastic anthrax epidemic model can enhance accuracy, evaluate control measures, and identify critical factors. The mathematical modeling of an anthrax disease includes the four-compartment
Wafa F. Alfwzan, Kinda Abuasbe, Ali Raza, Muhammad Rafiq, Muath Awadalla, Muna A. Almulla   +5 more
doaj   +2 more sources

Deterministic, stochastic, and mean-field PDE models in neuroscience [PDF]

Frontiers in Computational Neuroscience
Large neuronal networks demonstrate complex dynamics across multiple scales, ranging from single-neuron excitability and spike-train variability to mesoscopic rhythms and whole-brain activity.
Coşkun Çetin, Jose Roberto Castilho Piqueira, Burhaneddin İzgi, Ayse Peker-Dobie, Semra Ahmetolan, Murat Özkaya   +5 more
doaj   +2 more sources

Research on optimization of power grid load forecasting models based on deep learning [PDF]

Scientific Reports
Precise forecasting of power grid load is essential for maintaining the stability and efficiency of contemporary energy systems. Traditional statistical and machine learning methods often struggle to capture the nonlinear temporal dependencies and ...
Junhua Hu, Hao Huang, Wenjin Chen, Si Zhang, Daijun Xia, Silei Yao   +5 more
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Modelling Infectious Disease Dynamics: A Robust Computational Approach for Stochastic SIRS with Partial Immunity and an Incidence Rate

Mathematics, 2023
For decades, understanding the dynamics of infectious diseases and halting their spread has been a major focus of mathematical modelling and epidemiology.
Amani S. Baazeem, Yasir Nawaz, Muhammad Shoaib Arif, Kamaleldin Abodayeh, Mae Ahmed AlHamrani   +4 more
doaj   +1 more source

An Investigation into Accuracy, Computation, and Robustness of Stochastic Linearization of Systems with Saturation Nonlinearities

Results in Control and Optimization, 2021
Quasilinear Control (QLC) is a theory with a set of tools that can be used for the analysis and design of stochastic feedback control systems with nonlinear actuators and sensors.
Sarnaduti Brahma, Timothy Foley, Sam Wisotzki, Hamid R. Ossareh   +3 more
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Synthesis and Computer Study of Population Dynamics Controlled Models Using Methods of Numerical Optimization, Stochastization and Machine Learning [PDF]

Mathematics, 2021
The problems of synthesis and analysis of multidimensional controlled models of population dynamics are of both theoretical and applied interest. The need to solve numerical optimization problems for such a class of models is associated with the expansion of ecosystem control requirements.
Anastasia V. Demidova, Olga V. Druzhinina, Olga N. Masina, Alexey A. Petrov   +3 more
openaire   +3 more sources

Flexible Load Stochastic Optimal Control of Wind Power-Based Hydrogen Production and Ammonia Synthesis Systems Based on the Itô Process

Zhongguo dianli, 2023
Wind power-based hydrogen production and ammonia synthesis (P2A) is one of the potential technical routes for large-scale renewable energy consumption and carbon emission reduction in the power and chemical industries. Taking power-to-hydrogen production
Guoshan YANG, Jie ZHU, Wenqin SONG, Yiwei QIU, Buxiang ZHOU   +4 more
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Computational methods for stochastic control with metric interval temporal logic specifications [PDF]

2015 54th IEEE Conference on Decision and Control (CDC), 2015
8 pages, 6 figures, submitted to IEEE CDC ...
Jie Fu 0002, Ufuk Topcu
openaire   +3 more sources

Efficient Ensemble-Based Stochastic Gradient Methods for Optimization Under Geological Uncertainty

Frontiers in Earth Science, 2020
Ensemble-based stochastic gradient methods, such as the ensemble optimization (EnOpt) method, the simplex gradient (SG) method, and the stochastic simplex approximate gradient (StoSAG) method, approximate the gradient of an objective function using an ...
Hoonyoung Jeong, Hoonyoung Jeong, Hoonyoung Jeong, Alexander Y. Sun, Jonghyeon Jeon, Baehyun Min, Daein Jeong   +6 more
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A recursive Riccati interior-point method for chance-constrained stochastic model predictive control

SICE Journal of Control, Measurement, and System Integration, 2023
This study covers the model predictive control of linear discrete-time systems subject to stochastic additive disturbances and state chance constraints.
Jingyu Zhang, Toshiyuki Ohtsuka
doaj   +1 more source