Over the last 20 years Machine Learning (ML) has been applied to a wide variety of
applications in the fields of engineering and computer science. In the field of material …

Studies for sepsis prediction using machine learning are developing rapidly in medical
science recently. In this review, we propose a set of new evaluation criteria and reporting …

Non-affine parametric dependencies, nonlinearities and advection-dominated regimes of
the model of interest can result in a slow Kolmogorov n-width decay, which precludes the …

This study investigates the integration of machine learning (ML) and data assimilation (DA)
techniques, focusing on implementing surrogate models for Geological Carbon Storage …

Abstract Recently, deep Convolutional Neural Networks (CNNs) have proven to be
successful when employed in areas such as reduced order modeling of parametrized PDEs …

Variational autoencoders (VAEs) have shown promising potential as artificial neural
networks (NN) for developing reduced-order models (ROMs) in the context of turbulent …

Autoencoder-based reduced-order modeling (ROM) has recently attracted significant
attention, owing to its ability to capture underlying nonlinear features. However, two critical …

Computational fluid dynamics (CFD) models have been used for the simulation of urban
airflow and pollutant dispersion, due to their capability to capture different length scales and …

Abstract Machine learning (ML) has evolved as a technology used in even broader domains,
ranging from spam detection to space exploration, as a result of the boom in available data …

Within the framework of parameter dependent Partial Differential Equations (PDEs), we
develop a constructive approach based on Deep Neural Networks for the efficient …