Differentiable fluid simulators are increasingly demonstrating value as useful tools for
developing data-driven models in computational fluid dynamics (CFD). Differentiable …

Background: Computational cardiovascular flow modeling plays a crucial role in
understanding blood flow dynamics. While 3D models provide acute details, they are …

A wavelet-based machine learning method is proposed for predicting the time evolution of
homogeneous isotropic turbulence where vortex tubes are preserved. Three-dimensional …

High-fidelity computational fluid dynamics (CFD) simulations for design space explorations
can be exceedingly expensive due to the cost associated with resolving the finest scales …

In a post-ChatGPT world, this paper explores the potential of leveraging scalable artificial
intelligence for scientific discovery. We propose that scaling up artificial intelligence on high …

Cavitation is a common phenomenon in naval and ocean engineering, typically occurring in
the wakes of high-speed rotating propellers and on the surfaces of fast-moving underwater …

Tackling coupled sets of partial differential equations (PDEs) through scientific machine
learning presents a complex challenge, but it is essential for developing data-driven physics …

Identifying dynamical systems from experimental data is a notably difficult task. Prior
knowledge generally helps, but the extent of this knowledge varies with the application, and …