The field of dynamical systems is being transformed by the mathematical tools and
algorithms emerging from modern computing and data science. First-principles derivations …

Advances in experimental techniques and the ever-increasing fidelity of numerical
simulations have led to an abundance of data describing fluid flows. This review discusses a …

Data-driven discovery is revolutionizing how we model, predict, and control complex
systems. Now with Python and MATLAB®, this textbook trains mathematical scientists and …

The integration of data and scientific computation is driving a paradigm shift across the
engineering, natural, and physical sciences. Indeed, there exists an unprecedented …

Reduced order modeling (ROM) has been widely used to create lower order,
computationally inexpensive representations of higher-order dynamical systems. Using …

Nanofluids are fluid nanoparticle suspensions that exhibit enhanced properties at modest
nanoparticle concentrations. Nanofluids have unique heat transfer properties and are …

The Koopman theory, a concept to globally model nonlinear signals by a linear Hamiltonian,
has been at the frontier of fluid mechanics research for the last decade. Wind engineering …

This paper distills the serial work of Li et al., 2020a, Li et al., 2021, Li et al., 2022a, Li et al.,
2022b, Li et al., 2022c, Li et al., 2023a, Li et al., 2023b into a practice guide for the Dynamic …

This study applies the Dynamic Mode Decomposition (DMD) technique to a prototypical
wind engineering problem of flow past a square prism at a Reynolds number of 22,000 to …

Multi-phase modeling considering the nanofluid heterogeneity and slip velocity is not
explored in simulating nanofluid flow and heat transfer at higher Reynolds numbers (Re). A …