The use of machine learning algorithms to predict behaviors of complex systems is booming.
However, the key to an effective use of machine learning tools in multi-physics problems …

Data-driven methods have become an essential part of the methodological portfolio of fluid
dynamicists, motivating students and practitioners to gather practical knowledge from a …

Detailed numerical simulations of detailed combustion systems require substantial
computational resources, which limit their use for optimization and uncertainty quantification …

This current study investigated the relationship between soil organic carbon (SOC) and
terrain derivatives on soil developed on dissimilar lithology while comparing the best …

This work addresses the problem of implementing model predictive control (MPC) in
situations where the training data available for modeling contains possible correlations, and …

Operating variables, such as flow direction, velocity, temperature, and osmotic pressure,
affect the performance of forward osmosis (FO) systems. Water and salt permeabilities of the …

This work presents a new application of higher order dynamic mode decomposition
(HODMD) for the analysis of reactive flows. Due to the high complexity of the data analysed …

The fast and accurate reduced-order modeling of fluidized beds is a challenging task in the
field of fluid dynamics, owing to their high dimensionality and nonlinear dynamic behavior. In …

This work proposes an ensemble learning-based catalytic cracking coke yield prediction
model called the harmonic-ensembled extreme learning machine (HEELM). The model …

This study presents data-driven reduced-order models (ROMs) of a lunar orbiter based on
principal component analysis (PCA) and artificial neural networks (ANNs) for a ground …