403 citations · 1.2k across the 7 of their papers we have counts for
3 papers · 1 filter
Physics-informed neural networks (PINNs) for fluid mechanics: A review
Shengze Cai, Zhiping Mao, Zhicheng Wang +2
Despite the significant progress over the last 50 years in simulating flow problems using numerical discretization of the Navier-Stokes equations (NSE), we still cannot incorporate…
Flow over an espresso cup: Inferring 3D velocity and pressure fields from tomographic background oriented schlieren videos via physics-informed neural networks
Shengze Cai, Zhicheng Wang, Frederik Fuest +3
Tomographic background oriented schlieren (Tomo-BOS) imaging measures density or temperature fields in 3D using multiple camera BOS projections, and is particularly useful for inst…
Physics-informed neural networks with hard constraints for inverse design
Lu Lu, Raphael Pestourie, Wenjie Yao +3
Inverse design arises in a variety of areas in engineering such as acoustic, mechanics, thermal/electronic transport, electromagnetism, and optics. Topology optimization is a major…