Nonlinear extensions of linear inverse models under memoryless or persistent random forcing
This study extends the linear inverse modeling (LIM) framework to nonlinear settings by presenting White-nLIM and Colored-nLIM, statistics-based empirical methods that construct approximate stochastic systems incorporating quadratic deterministic dynamics with either memoryless Gaussian white noise...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
American Physical Society
2025-08-01
|
| Series: | Physical Review Research |
| Online Access: | http://doi.org/10.1103/ds1j-fx3v |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849232884799373312 |
|---|---|
| author | Justin Lien Hiroyasu Ando |
| author_facet | Justin Lien Hiroyasu Ando |
| author_sort | Justin Lien |
| collection | DOAJ |
| description | This study extends the linear inverse modeling (LIM) framework to nonlinear settings by presenting White-nLIM and Colored-nLIM, statistics-based empirical methods that construct approximate stochastic systems incorporating quadratic deterministic dynamics with either memoryless Gaussian white noise or persistent Ornstein-Uhlenbeck colored noise. Beyond the evident improvements over linear models, Colored-nLIM offers a robust approach to parameter estimation and statistical modeling under persistent stochastic forcing. Together with White-nLIM, these methods provide a systematic framework to assess the role of noise persistence in inverse modeling. Applications to the Lorenz 63 system and a simplified El Niño-Southern Oscillation model demonstrate their potential to capture chaotic behavior and climate variability. |
| format | Article |
| id | doaj-art-27df3d8ae16c45fbb11a588c8b57b1e4 |
| institution | Kabale University |
| issn | 2643-1564 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | American Physical Society |
| record_format | Article |
| series | Physical Review Research |
| spelling | doaj-art-27df3d8ae16c45fbb11a588c8b57b1e42025-08-20T16:35:06ZengAmerican Physical SocietyPhysical Review Research2643-15642025-08-0173L03203810.1103/ds1j-fx3vNonlinear extensions of linear inverse models under memoryless or persistent random forcingJustin LienHiroyasu AndoThis study extends the linear inverse modeling (LIM) framework to nonlinear settings by presenting White-nLIM and Colored-nLIM, statistics-based empirical methods that construct approximate stochastic systems incorporating quadratic deterministic dynamics with either memoryless Gaussian white noise or persistent Ornstein-Uhlenbeck colored noise. Beyond the evident improvements over linear models, Colored-nLIM offers a robust approach to parameter estimation and statistical modeling under persistent stochastic forcing. Together with White-nLIM, these methods provide a systematic framework to assess the role of noise persistence in inverse modeling. Applications to the Lorenz 63 system and a simplified El Niño-Southern Oscillation model demonstrate their potential to capture chaotic behavior and climate variability.http://doi.org/10.1103/ds1j-fx3v |
| spellingShingle | Justin Lien Hiroyasu Ando Nonlinear extensions of linear inverse models under memoryless or persistent random forcing Physical Review Research |
| title | Nonlinear extensions of linear inverse models under memoryless or persistent random forcing |
| title_full | Nonlinear extensions of linear inverse models under memoryless or persistent random forcing |
| title_fullStr | Nonlinear extensions of linear inverse models under memoryless or persistent random forcing |
| title_full_unstemmed | Nonlinear extensions of linear inverse models under memoryless or persistent random forcing |
| title_short | Nonlinear extensions of linear inverse models under memoryless or persistent random forcing |
| title_sort | nonlinear extensions of linear inverse models under memoryless or persistent random forcing |
| url | http://doi.org/10.1103/ds1j-fx3v |
| work_keys_str_mv | AT justinlien nonlinearextensionsoflinearinversemodelsundermemorylessorpersistentrandomforcing AT hiroyasuando nonlinearextensionsoflinearinversemodelsundermemorylessorpersistentrandomforcing |