In simulation-based realization of complex systems, we are forced to address the issue of
computational complexity. One critical issue that must be addressed is the approximation of …

Abstract The Surrogate Modeling Toolbox (SMT) is an open-source Python package that
offers a collection of surrogate modeling methods, sampling techniques, and a set of sample …

Minimization with orthogonality constraints (eg, X^ ⊤ X= I) and/or spherical constraints (eg,
‖ x ‖ _2= 1) has wide applications in polynomial optimization, combinatorial optimization …

The volatility of financial returns changes over time and, for the last thirty years, Generalized
Autoregressive Conditional Heteroscedasticity (GARCH) models have provided the principal …

Dieses Buch ist ein handlicher und praktischer Leitfaden zur Monte Carlo Simulation (MCS).
Er gibt eine Einführung in Standardmethoden und fortgeschrittene Verfahren, um die …

The increasing penetration of renewable generation in power systems necessitates the
modeling of this stochastic system infeed in operation and planning studies. The system …

Predicting heart disease is regarded as one of the most difficult challenges in the health-
care profession. To predict cardiac disease, researchers employed a variety of algorithms …

We propose a new class of observation-driven time-varying parameter models for dynamic
volatilities and correlations to handle time series from heavy-tailed distributions. The model …

This paper introduces a data-driven process for designing and fabricating materials with
desired deformation behavior. Our process starts with measuring deformation properties of …

Due to the accessibility of data with multiple features, many feature determination
techniques available in written form. These features promote data with extremely high …