Aiming to find the most suitable development schemes of conventional and unconventional
reservoirs for maximum energy supply or economic benefits, reservoir production …

This study proposes a deep learning framework to perform uncertainty quantification of vibro-
acoustic coupling problems for robot manta rays. First, Loop subdivision surfaces are used …

Physics-Informed Machine Learning (PIML) is an emerging computing paradigm that offers a
new approach to tackle multiphysics modeling problems prevalent in the field of chemical …

Licensing the next-generation of nuclear reactor designs requires extensive use of Modeling
and Simulation (M&S) to investigate system response to many operational conditions …

The simulation and prediction of fluid flow in porous media play a profoundly significant role
in today's scientific and engineering domains, particularly in gaining a deeper …

The weaker transverse mechanical response of unidirectional carbon fiber reinforced
polymer (UD-CFRP) composites has raised a lot of attention and research. The single-task …

Predicting the trajectories of hydraulic fractures in unconventional reservoirs poses a
significant challenge due to the heterogeneous nature of the rock matrix. Obtaining an …

The characterization of fracture networks is challenging for enhanced geothermal systems,
yet is crucial for the understanding of the thermal distributions, and the behaviors of flow field …

Digital twins (DTs) have attracted widespread attention in academia and industry in recent
years. It can accurately reflect the physical world in real-time, enabling online monitoring …

In this paper, a nonlocal energy-informed neural network is proposed to deal with elastic
solids containing discontinuities by considering the long-range interactions of material …