67 citations · 97 across the 6 of their papers we have counts for
6 papers
Open-source BOS tomography dataset of high-speed flow over a flight body
Joseph P. Molnar, Amit K. Singh, Christopher J. Clifford +4
We present an open-source background-oriented schlieren dataset with 70 views of high-speed flow over a flight body. Sample analyses are performed using a neural-implicit reconstru…
Unsupervised neural-implicit laser absorption tomography for quantitative imaging of unsteady flames
Joseph P. Molnar, Jiangnan Xia, Rui Zhang +2
This paper presents a novel neural-implicit approach to laser absorption tomography (LAT) with an experimental demonstration. A coordinate neural network is used to represent therm…
Neural optical flow for planar and stereo PIV
Andrew I. Masker, Ke Zhou, Joseph P. Molnar +1
Neural optical flow (NOF) offers improved accuracy and robustness over existing OF methods for particle image velocimetry (PIV). Unlike other OF techniques, which rely on discrete…
Forward and inverse modeling of depth-of-field effects in background-oriented schlieren
Joseph P. Molnar, Elijah J. LaLonde, Christopher S. Combs +3
We report a novel "cone-ray" model of background-oriented schlieren (BOS) imaging that accounts for depth-of-field effects. Reconstructions of the density field performed with this…
Estimating density, velocity, and pressure fields in supersonic flow using physics-informed BOS
Joseph P. Molnar, Lakshmi Venkatakrishnan, Bryan E. Schmidt +2
We report a new workflow for background-oriented schlieren (BOS), termed "physics-informed BOS," to extract density, velocity, and pressure fields from a pair of reference and dist…
Flow field tomography with uncertainty quantification using a Bayesian physics-informed neural network
Joseph P. Molnar, Samuel J. Grauer
We report a new approach to flow field tomography that uses the Navier-Stokes and advection-diffusion equations to regularize reconstructions. Tomography is increasingly employed t…