Abstract The use of Machine Learning (ML) has rapidly spread across several fields of
applied sciences, having encountered many applications in Structural Dynamics and …

Segregated flow, including stratified and annular flows, is commonly encountered in several
practical applications such as chemical, nuclear, refrigeration, and oil and gas industries …

We show that the one-dimensional (1D) two-fluid model (TFM) for stratified flow in channels
and pipes (in its incompressible, isothermal form) satisfies an energy conservation equation …

In combustion simulations, model discrepancies result from employing simplified physics or
chemistry closure models for achieving a balance between easiness of computation and …

The one-dimensional incompressible two-fluid model is a dynamic model for two-phase flow
in pipes, which resolves only cross-sectionally averaged quantities. It can be used to predict …

The challenges related to liquid loading have been observed during flow-back after
hydraulic fracturing, as well as during the production phase, and are further aggravated with …

This review explores the application of grey-box modelling, specifically Physics-Informed
Machine Learning (PIML), in the context of Structural Health Monitoring (SHM) within civil …

Machine learning and deep learning techniques have gained significant attention in recent
years for enhancing the precision and efficiency of velocity estimation in multiphase flow …

In this thesis, a probabilistic embedded discrepancy approach to understanding the limits of
the use of reduced chemical kinetics in computational combustion models and also to …

An apparatus for reducing an error of a physical model using an artificial intelligence
algorithm is provided. The apparatus for reducing an error of a physical model includes: a …