Results 31 to 40 of about 642 (180)

Stochastic collocation-based finite element of structural nonlinear dynamics with application in composite structures

MATEC Web of Conferences, 2016
Stochastic analysis of structures having nonlinearity by means of sampling methods leads to expensive cost in term of computational time. In contrast, non-sampling methods based on the spectral representation of uncertainty are very efficient with ...
Sepahvand K.
doaj   +1 more source

Deterministic global optimization algorithm based on outer approximation for the parameter estimation of nonlinear dynamic biological systems

BMC Bioinformatics, 2012
Background The estimation of parameter values for mathematical models of biological systems is an optimization problem that is particularly challenging due to the nonlinearities involved.
Miró Anton, Pozo Carlos, Guillén-Gosálbez Gonzalo, Egea Jose A, Jiménez Laureano   +4 more
doaj   +1 more source

Residual Strength Prediction of Chemically Reactive Two‐Phase Nanofluid Flow in a Bingham–Papanastasiou Rheological Theory Using a Morlet‐Based Wavelet Neural Network Approach

Asia-Pacific Journal of Chemical Engineering, EarlyView.
ABSTRACT The behavior of nanofluid flow involving a zero‐mass flux condition has received considerable interest because of a realistic scenario. In reality, this condition confines the optimistic accumulation or disappearance of nanoparticles past a sheet, constructing a more physically realistic demonstration through several applications, such as heat
Umair Khan, Hamza Rauf, Aurang Zaib, J. K. Madhukesh   +3 more
wiley   +1 more source

Automated Data‐Efficient Symbolic Regression for Interpretable Bioprocess Model Development

Biotechnology and Bioengineering, EarlyView.
ABSTRACT Bioprocessing is central to the sustainable manufacture of pharmaceuticals, food products, and renewable chemicals. Consequently, developing high‐fidelity kinetic models to facilitate accurate process prediction, optimisation, and scale‐up is a top research priority.
Luca Riezzo, Alexander Rogers, Harry Kay, Dongda Zhang   +3 more
wiley   +1 more source

Learning Rocking Dynamics From Sparse Shake‐Table Data With Interpretable Physics‐Informed Neural Networks

Earthquake Engineering &Structural Dynamics, EarlyView.
ABSTRACT We present a hybrid interpretable Physics‐Informed Neural Network Long‐Short Term Memory (Hybrid PINN LSTM) framework for predicting the seismic response of rocking blocks. Existing analytical models rely on uncertain idealizations, while purely data‐driven and machine‐learning approaches lack physical consistency and interpretability.
Shirley Shen, Christian Málaga‐Chuquitaype   +1 more
wiley   +1 more source

Integral Reinforcement Learning-Based Stochastic Guaranteed Cost Control for Time-Varying Systems with Asymmetric Saturation Actuators

Actuators
This study explores a stochastic guarantee cost control (GCC) for time-varying systems with random parameters and asymmetric saturation actuators by employing the integral reinforcement learning (IRL) method in the dynamic event-triggered (DET) mode ...
Yuling Liang, Mengjia Xie, Juan Zhang, Zhongyang Ming, Zhiyun Gao   +4 more
doaj   +1 more source

Approximation of stochastic partial differential equations by a kernel-based collocation method [PDF]

International Journal of Computer Mathematics, 2012
Updated Version in International Journal of Computer Mathematics, Closed to Ye's Doctoral ...
Igor Cialenco, Gregory E. Fasshauer, Qi Ye 0007   +2 more
openaire   +2 more sources

Predicting EU Emissions Allowance Prices Using Macroeconomic Indicators and Hybrid AI Models

Journal of Forecasting, EarlyView.
ABSTRACT Predicting carbon allowance prices has grown more crucial in relation to carbon market regulation, financial strategy, and environmental policy development. This study examines a hybrid forecasting system that combines deep learning with ensemble machine learning models to forecast the price fluctuations of EU Emissions Allowance (EUAs) within
Saptarshi Ganguly, Pooja Sengupta, Divya Aggarwal   +2 more
wiley   +1 more source

A dramaturgy of uncertainty: Transdisciplinary manoeuvres across forestry and theatre

People and Nature, EarlyView.
Abstract The uncertainties of climate change mean that forestry adaptation strategies are often complex and contested. Research has suggested that there is an interest in the forestry sector for facilitated dialogue about uncertainty (de Pellegrin Llorente et al., 2023).
Rachel Clive, Deirdre Heddon, Jordan Rydlewski, David Edwards, Chris Fremantle   +4 more
wiley   +1 more source

Numerical method for simulation of quadratic Riccati differential equations

Recent Advances in Natural Sciences
Differential equations widely applied in various fields of engineering and mathematical studies, particularly in control theory, optimal control, and stochastic realization. Numerous methods have been proposed for their solution.
Adam Ajimoti Ishaq, Folashade Mistura Jimoh, Kazeem Issa   +2 more
doaj   +1 more source