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...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
American Physical Society
2025-08-01
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| Series: | Physical Review Research |
| Online Access: | http://doi.org/10.1103/ds1j-fx3v |
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