The recently proposed swarm intelligence algorithm, Runge–Kutta Optimization (RUN), is
rooted in the fourth-order Runge–Kutta method. Compared with its counterparts, RUN …

Reservoir history matching represents a crucial stage in the reservoir development process
and purposes to match model predictions with various observed field data, including …

Inspired by the evolutionary process in nature, Differential Evolution (DE) has been widely
concerned and used as a numerical global optimizer for decades of years, since its …

Reservoir history matching refers to the process of continuously adjusting the parameters of
the reservoir model, so that its dynamic response will match the historical observation data …

History matching is a crucial process that enables the calibration of uncertain parameters of
the numerical model to obtain an acceptable match between simulated and observed …

For history-matching problems, simulations of reservoir models usually involve high
computational costs. Surrogate modeling based on deep learning has proved to be an …

Inverse modeling can provide a reliable geological model for subsurface flow numerical
simulation, which is a challenging issue that requires calibration of the uncertain parameters …

Operating data-driven inverse design (DID) provides updated knowledge for the forward
design process. It forms a closed-loop of design enhancements in which uncertainties from …

To solve the problems of slow convergence and limited prediction ability of the Differential
Evolution (DE) algorithm, an enhanced DE algorithm with the trustworthiness of a dynamic …

With the rapid development of computer and information technology, people can obtain and
store data in a faster and cheaper way, which makes the amount of data and information …