109 citations · 292 across the 11 of their papers we have counts for
11 papers
Biology-informed neural networks learn nonlinear representations from omics data to improve genomic prediction and interpretability
Katiana Kontolati, Rini Jasmine Gladstone, Ian Davis +1
We extend biologically-informed neural networks (BINNs) for genomic prediction (GP) and selection (GS) in crops by integrating thousands of single-nucleotide polymorphisms (SNPs) w…
Bispectral decomposition and energy transfer in a turbulent jet
Akhil Nekkanti, Ethan Pickering, Oliver T. Schmidt +1
We employ bispectral mode decomposition (BMD) to investigate coherent triadic interactions and nonlinear energy transfer in a subsonic turbulent jet. BMD extracts the flow structur…
The effect of flight on a turbulent jet: coherent structure eduction and resolvent analysis
Igor A. Maia, Liam Heidt, Ethan Pickering +3
We study coherent structures in subsonic turbulent jets subject to a flight stream. A thorough characterisation of the effects of a flight stream on the turbulent field was recentl…
Information FOMO: The unhealthy fear of missing out on information. A method for removing misleading data for healthier models
Ethan Pickering, Themistoklis P. Sapsis
Misleading or unnecessary data can have out-sized impacts on the health or accuracy of Machine Learning (ML) models. We present a Bayesian sequential selection method, akin to Baye…
Discovering and forecasting extreme events via active learning in neural operators
Ethan Pickering, Stephen Guth, George Em Karniadakis +1
Extreme events in society and nature, such as pandemic spikes, rogue waves, or structural failures, can have catastrophic consequences. Characterizing extremes is difficult as they…
Structure and Distribution Metric for Quantifying the Quality of Uncertainty: Assessing Gaussian Processes, Deep Neural Nets, and Deep Neural Operators for Regression
Ethan Pickering, Themistoklis P. Sapsis
We propose two bounded comparison metrics that may be implemented to arbitrary dimensions in regression tasks. One quantifies the structure of uncertainty and the other quantifies…
Efficient global resolvent analysis via the one-way Navier-Stokes equations. Part 2. Optimal response
Georgios Rigas, Omar Kamal, Aaron Towne +1
In this study, we develop an efficient approach for approximating resolvent modes via spatial marching. Building on the methodology from Part 1, we leverage the ability of the proj…
Efficient global resolvent analysis via the one-way Navier-Stokes equations. Part 1. Forced response
Aaron Towne, Georgios Rigas, Ethan Pickering +1
Resolvent analysis is a powerful tool for modeling and analyzing turbulent flows and in particular provides an approximation of coherent flow structures. Despite recent algorithmic…
Resolvent-based modeling of turbulent jet noise
Ethan Pickering, Aaron Towne, Peter Jordan +1
Resolvent analysis has demonstrated encouraging results for modeling coherent structures in jets when compared against their data-educed counterparts from high-fidelity large-eddy…
Optimal eddy viscosity for resolvent-based models of coherent structures in turbulent jets
Ethan Pickering, Georgios Rigas, Oliver T. Schmidt +2
Response modes computed via linear resolvent analysis of a turbulent mean-flow field have been shown to qualitatively capture characteristics of the observed turbulent coherent str…
Lift-up, Kelvin-Helmholtz and Orr mechanisms in turbulent jets
Ethan Pickering, Georgios Rigas, Petrônio A. S. Nogueira +3
Three amplification mechanisms present in turbulent jets, namely lift-up, Kelvin-Helmholtz, and Orr, are characterized via global resolvent analysis and spectral proper orthogonal…