Over the last decade, deep learning (DL), a branch of machine learning, has experienced
rapid progress. Powerful tools for tasks that have been traditionally complex to automate …

Time-dependent partial differential equations (PDEs) are ubiquitous in science and
engineering. Recently, mostly due to the high computational cost of traditional solution …

Partial differential equations (PDEs) see widespread use in sciences and engineering to
describe simulation of physical processes as scalar and vector fields interacting and …

Partial differential equations (PDEs) are central to describing complex physical system
simulations. Their expensive solution techniques have led to an increased interest in deep …

The simulation of fluid dynamics, typically by numerically solving partial differential
equations, is an essential tool in many areas of science and engineering. However, the high …

Machine learning-based modeling of physical systems has attracted significant interest in
recent years. Based solely on the underlying physical equations and initial and boundary …

In vehicle shape design from aerodynamic performance perspective, deep learning methods
enable us to estimate the flow field in a short period. However, the estimated flow fields are …

In this work, the feasibility of applying a digital twin combined with machine learning
algorithms (convolutional neural network and random forest classifier) to predict the …

Physics informed neural networks (PINNs) have demonstrated their effectiveness in solving
partial differential equations (PDEs). By incorporating the governing equations and …

A deep learning approach is presented for heat transfer and pressure drop prediction of
complex fin geometries generated using composite Bézier curves. Thermal design process …