There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …

The recent development of machine learning (ML) and Deep Learning (DL) increases the
opportunities in all the sectors. ML is a significant tool that can be applied across many …

There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …

This chapter introduces a framework for combining scientific knowledge of physics-based
models with neural networks to advance scientific discovery. It explains termed physics …

Physics-based models are often used to study engineering and environmental systems. The
ability to model these systems is the key to achieving our future environmental sustainability …

This paper presents a literature review on methods for enabling real-time analysis in digital
twins, which are virtual models of physical systems. The advantages of digital twins are …

In this paper, we introduce a modular deep neural network (DNN) framework for data-driven
reduced order modeling of dynamical systems relevant to fluid flows. We propose various …

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

This paper proposes a supervised machine learning framework for the non-intrusive model
order reduction of unsteady fluid flows to provide accurate predictions of non-stationary state …

This paper presents a novel model reduction method based on proper orthogonal
decomposition and temporal convolutional neural network. The method generates basis …