Cargando…
Improving reduced-order models through nonlinear decoding of projection-dependent outputs
A fundamental hindrance to building data-driven reduced-order models (ROMs) is the poor topological quality of a low-dimensional data projection. This includes behavior such as overlapping, twisting, or large curvatures or uneven data density that can generate nonuniqueness and steep gradients in qu...
Autores principales: | Zdybał, Kamila, Parente, Alessandro, Sutherland, James C. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10682754/ https://www.ncbi.nlm.nih.gov/pubmed/38035196 http://dx.doi.org/10.1016/j.patter.2023.100859 |
Ejemplares similares
-
Cost function for low-dimensional manifold topology assessment
por: Zdybał, Kamila, et al.
Publicado: (2022) -
Identification of nonlinear-nonlinear neuron models and stimulus decoding
por: Lazar, Aurel A, et al.
Publicado: (2013) -
Revealing nonlinear neural decoding by analyzing choices
por: Yang, Qianli, et al.
Publicado: (2021) -
Reduced-order modelling of parameter-dependent, linear and nonlinear dynamic partial differential equation models
por: Shah, A. A., et al.
Publicado: (2017) -
Reconstruction of noise-driven nonlinear networks from node outputs by using high-order correlations
por: Chen, Yang, et al.
Publicado: (2017)