The scaling up of fluidized beds has been purposefully pursued for more than 100 years.
Yet, over that time, scale-up tools have not significantly changed. Data analysis is typically a …

Solving partial differential equations (PDEs) is the core of many fields of science and
engineering. While classical approaches are often prohibitively slow, machine learning …

Neural operators have recently grown in popularity as Partial Differential Equation (PDE)
surrogate models. Learning solution functionals, rather than functions, has proven to be a …

Adaptive meshes are pivotal in numerical modeling and simulation, offering a means to
efficiently, precisely, and flexibly represent intricate physical phenomena, particularly when …

Numerically solving partial differential equations typically requires fine discretization to
resolve necessary spatiotemporal scales, which can be computationally expensive. Recent …

Despite more than 100 years of commercialization of wide-ranging fluidized bed reactors,
scale-up tools and methods have remained quite similar. To exploit the benefits of fluidized …

Unmanned aerial vehicles (UAVs) combined with mobile edge computing (MEC) servers
assist ground terminals (GTs) for communication and computation in wireless networks …

Reinforcement learning (RL) has emerged as a promising approach to automating decision
processes. This paper explores the application of RL techniques to optimise the polynomial …

Human breathing is crucial for studying indoor environments and human health.
Computational Fluid Dynamics (CFD) is a key tool for simulating human respiration. To …

Metallurgical reactors, especially in ironmaking/steelmaking process, characterise with high-
temperature turbulence, multiphase flow, mass/heat transfer and reactions. Computational …