This review covers the new developments in machine learning (ML) that are impacting the
multi-disciplinary area of aerospace engineering, including fundamental fluid dynamics …

Artificial intelligence (AI), machine learning (ML), and data science are leading to a
promising transformative paradigm. ML, especially deep learning and physics-informed ML …

Computational fluid dynamics (CFD) represents a valuable tool in the design process of built
environments, enhancing the comfort, health, energy efficiency, and safety of indoor and …

Physical systems are governed by partial differential equations (PDEs). The Navier-Stokes
equations describe fluid flows and are representative of nonlinear physical systems with …

Ordinary differential equations (ODEs) are extremely important in modeling dynamic
systems, such as chemical reaction networks. However, many challenges exist for building …

A data-based reduced-order model (ROM) is developed to accelerate the time integration of
stiff chemically reacting systems by effectively removing the stiffness arising from a wide …

This study introduces the gradient boosted decision tree (GBDT) as a machine learning
approach to circumvent the need for a direct integration of the typically stiff system of …

Kinetic model identification relies on accurate concentration measurements and physical
constraints to limit solution multiplicity. Not having these measurements and prior knowledge …

The digitalization of chemical research and industry is vastly increasing the available data
for developing and parametrizing kinetic models. To exploit this data, machine learning …

The large-scale simulation of dynamical systems is critical in numerous scientific and
engineering disciplines. However, traditional numerical solvers are limited by the choice of …