Super-resolution generative adversarial networks of randomly-seeded fields
Reconstruction of field quantities from sparse measurements is a problem arising in a broad
spectrum of applications. This task is particularly challenging when the mapping between …
spectrum of applications. This task is particularly challenging when the mapping between …
Gappy spectral proper orthogonal decomposition
A Nekkanti, OT Schmidt - Journal of Computational Physics, 2023 - Elsevier
Experimental spatio-temporal flow data often contain gaps or other types of undesired
artifacts. To reconstruct flow data in the compromised or missing regions, a data completion …
artifacts. To reconstruct flow data in the compromised or missing regions, a data completion …
Computational Sensing, Understanding, and Reasoning: An Artificial Intelligence Approach to Physics-Informed World Modeling
This work offers a discussion on how computational mechanics and physics-informed
machine learning can be integrated into the process of sensing, understanding, and …
machine learning can be integrated into the process of sensing, understanding, and …
Machine learning for flow field measurements: a perspective
S Discetti, Y Liu - Measurement Science and Technology, 2022 - iopscience.iop.org
Advancements in machine-learning (ML) techniques are driving a paradigm shift in image
processing. Flow diagnostics with optical techniques is not an exception. Considering the …
processing. Flow diagnostics with optical techniques is not an exception. Considering the …
Fusing sensor data with CFD results using gappy POD
In this study, the gappy Proper Orthogonal Decomposition (POD) method is adopted to fuse
wind-tunnel measured pressure and computational fluid dynamics (CFD) simulation results …
wind-tunnel measured pressure and computational fluid dynamics (CFD) simulation results …
Adaptive neuro-fuzzy inference system based data interpolation for particle image velocimetry in fluid flow applications
This paper presents an adaptive neuro-fuzzy inference system (ANFIS) approach for
recovering the missing velocity vectors that commonly occur during fluid flow measurements …
recovering the missing velocity vectors that commonly occur during fluid flow measurements …
On PIV random error minimization with optimal POD-based low-order reconstruction
Random noise removal from particle image velocimetry (PIV) data and spectra is of
paramount importance, especially for the computation of derivative quantities and spectra …
paramount importance, especially for the computation of derivative quantities and spectra …
Reconstruction of flow field with missing experimental data of a circular cylinder via machine learning algorithm
In this study, artificial neural networks (ANNs) have been implemented to recover missing
data from the particle image velocimetry (PIV), providing quantitative measurements of …
data from the particle image velocimetry (PIV), providing quantitative measurements of …
Development and evaluation of gappy-POD as a data reconstruction technique for noisy PIV measurements in gas turbine combustors
P Saini, CM Arndt, AM Steinberg - Experiments in Fluids, 2016 - Springer
Low signal-to-noise in particle image velocimetry (PIV) measurements in systems such as
high pressure gas turbine combustors can result in significant data gaps that negatively …
high pressure gas turbine combustors can result in significant data gaps that negatively …
Missing data recovery using data fusion of incomplete complementary data sets: A particle image velocimetry application
X Wen, Z Li, D Peng, W Zhou, Y Liu - Physics of Fluids, 2019 - pubs.aip.org
A data-fusion approach is reported to reconstruct missing data and is applied to particle
image velocimetry (PIV) measurements. This approach departs from the existing ones in that …
image velocimetry (PIV) measurements. This approach departs from the existing ones in that …