Feature engineering and symbolic regression methods for detecting hidden physics from sparse sensor observation data
We put forth a modular approach for distilling hidden flow physics from discrete and sparse
observations. To address functional expressiblity, a key limitation of the black-box machine …
observations. To address functional expressiblity, a key limitation of the black-box machine …
Nonlinear System Discovery and Machine Learning for Dynamical Systems
SAS Romeo - 2023 - search.proquest.com
Extracting physics from data has become a crucial task in fields where abundant data is
available. However, the underlying governing equations, physical laws, or models based on …
available. However, the underlying governing equations, physical laws, or models based on …
Identification of Physical Processes via Data Driven Methods
HVR Vaddireddy - 2020 - search.proquest.com
Extracting governing equations from data can be viewed as reverse engineering of Nature-
using data to identify the physical laws/models. This approach is crucial for fields where data …
using data to identify the physical laws/models. This approach is crucial for fields where data …