Deep reinforcement learning (DRL) has been applied to a variety of problems during the
past decade and has provided effective control strategies in high-dimensional and non …

Reduced-order modeling (ROM) of fluid flows has been an active area of research for
several decades. The huge computational cost of direct numerical simulations has motivated …

The low boiling temperature of dielectric fluids makes them suitable for satisfying the cooling
requirements of advanced high-energy-density devices commonly found in microelectronics …

A novel data-driven modal analysis method, reduced-order variational mode decomposition
(RVMD), is proposed, inspired by the Hilbert–Huang transform and variational mode …

Different types of neural networks have been used to solve the flow sensing problem in
turbulent flows, namely to estimate velocity in wall-parallel planes from wall measurements …

Big data and machine learning are driving comprehensive economic and social
transformations and are rapidly re-shaping the toolbox and the methodologies of applied …

Insufficient understanding of complex flow structures in multi-shaft stirred reactors hinders
industrial adoption. In this work, Proper Orthogonal Decomposition (POD) and Dynamic …

Accurate calculations of thermophysical properties (TPP) and fluid transport properties are
crucial for solving and enhancing a myriad of heat and mass transfer issues, particularly in …

This study explores the potential of hexagonal boron nitride (h-BN) nanoparticles
suspended in Deep Eutectic Solvent (DES) as a thermal medium or coolant. The eutectic …

The hybrid neural differentiable models mark a significant advancement in the field of
scientific machine learning. These models, integrating numerical representations of known …