This review discusses Operator Inference, a nonintrusive reduced modeling approach that
incorporates physical governing equations by defining a structured polynomial form for the …

Aerodynamic modeling plays an important role in multiphysics and design problems, in
addition to experiment and numerical simulation, due to its low-dimensional representation …

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

In recent years, we have witnessed a significant shift toward ever-more complex and ever-
larger-scale systems in the majority of the grand societal challenges tackled in applied …

In many situations across computational science and engineering, multiple computational
models are available that describe a system of interest. These different models have varying …

Predicting the evolution of systems with spatio-temporal dynamics in response to external
stimuli is essential for scientific progress. Traditional equations-based approaches leverage …

Numerical simulation of parametrized differential equations is of crucial importance in the
study of real-world phenomena in applied science and engineering. Computational methods …

Engineering is evolving in the same way than society is doing. Nowadays, data is acquiring
a prominence never imagined. In the past, in the domain of materials, processes and …

This work presents a nonintrusive projection-based model reduction approach for full
models based on time-dependent partial differential equations. Projection-based model …

In this work we present a Reduced Order Model which is specifically designed to deal with
turbulent flows in a finite volume setting. The method used to build the reduced order model …