Enabling Equation-Free Modeling via Diffusion Maps
Equation-free modeling aims at extracting low-dimensional macroscopic dynamics from complex high-dimensional systems that govern the evolution of microscopic states. This algorithm relies on lifting and restriction operators that map macroscopic states to microscopic states and vice versa. Combined...
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| Veröffentlicht in: | Journal of dynamics and differential equations 2024-02, Vol.36 (Suppl 1), p.415-434 |
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| container_title | Journal of dynamics and differential equations |
| container_volume | 36 |
| creator | Chin, Tracy Ruth, Jacob Sanford, Clayton Santorella, Rebecca Carter, Paul Sandstede, Björn |
| description | Equation-free modeling aims at extracting low-dimensional macroscopic dynamics from complex high-dimensional systems that govern the evolution of microscopic states. This algorithm relies on lifting and restriction operators that map macroscopic states to microscopic states and vice versa. Combined with simulations of the microscopic state, this algorithm can be used to apply Newton solvers to the implicitly defined low-dimensional macroscopic system or solve it more efficiently using direct numerical simulations. The key challenge is the construction of the lifting and restrictions operators that usually require a priori insight into the underlying application. In this paper, we design an application-independent algorithm that uses diffusion maps to construct these operators from simulation data. Code is available at
https://doi.org/10.5281/zenodo.5793299
. |
| doi_str_mv | 10.1007/s10884-021-10127-w |
| format | Article |
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https://doi.org/10.5281/zenodo.5793299
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https://doi.org/10.5281/zenodo.5793299
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https://doi.org/10.5281/zenodo.5793299
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| source | Springer Nature - Complete Springer Journals |
| subjects | Algorithms Applications of Mathematics Computer simulation Direct numerical simulation Mathematical models Mathematics Mathematics and Statistics Operators Ordinary Differential Equations Partial Differential Equations |
| title | Enabling Equation-Free Modeling via Diffusion Maps |
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