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Title: Data-Driven Computational Methods for Quasi-Stationary Distribution and Sensitivity Analysis
This paper studies computational methods for quasi-stationary distributions (QSDs). We first proposed a data-driven solver that solves Fokker–Planck equations for QSDs. Similar to the case of Fokker–Planck equations for invariant probability measures, we set up an optimization problem that minimizes the distance from a low-accuracy reference solution, under the constraint of satisfying the linear relation given by the discretized Fokker–Planck operator. Then we use coupling method to study the sensitivity of a QSD against either the change of boundary condition or the diffusion coefficient. The 1-Wasserstein distance between a QSD and the corresponding invariant probability measure can be quantitatively estimated. Some numerical results about both computation of QSDs and their sensitivity analysis are provided.  more » « less
Award ID(s):
2108628
NSF-PAR ID:
10345334
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Journal of Dynamics and Differential Equations
ISSN:
1040-7294
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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