OMB Individually Reported
Computationally efficient emulation of spheroidal elastic deformation sources using machine learning [2024 INV#WO0000000109302]
Low riskExact public inventory row
Description
analytical models are fast but can be inaccurate as they do not correctly satisfy boundary conditions for many geometries, while numerical models are slow and may require specialized expertise and software
Detailed example
output of a finite element numerical model with high fidelity
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
b) Pilot – The use case has been deployed in a limited test or pilot capacity.
Expected benefit
we trained supervised machine learning emulators (model surrogates) based on parallel partial Gaussian processes which predict the output of a finite element numerical model with high fidelity
Controls / human review
ATO: No; PIA: Not published
Data needed
model output