Strong Approximation of Stochastic Semiclassical Schroedinger Equation with Multiplicative Noise
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866913470931271680 |
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| author | Ji, Lihai Liu, Zhihui |
| author_facet | Ji, Lihai Liu, Zhihui |
| contents | We consider the stochastic nonlinear Schroedinger equation driven by a multiplicative noise in a semiclassical regime, where the Plank constant is small. In this regime, the solution of the equation exhibits high-frequency oscillations. We design an efficient numerical method combining the spectral Galerkin approximation and the midpoint scheme. This accurately approximates the solution, or at least of the associated physical observables. Furthermore, the strong convergence rate for the proposed scheme is derived, which explicitly depends on the Planck constant. This conclusion implies the semiclassical regime's admissible meshing strategies for obtaining "correct" physical observables. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2408_09118 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Strong Approximation of Stochastic Semiclassical Schroedinger Equation with Multiplicative Noise Ji, Lihai Liu, Zhihui Numerical Analysis We consider the stochastic nonlinear Schroedinger equation driven by a multiplicative noise in a semiclassical regime, where the Plank constant is small. In this regime, the solution of the equation exhibits high-frequency oscillations. We design an efficient numerical method combining the spectral Galerkin approximation and the midpoint scheme. This accurately approximates the solution, or at least of the associated physical observables. Furthermore, the strong convergence rate for the proposed scheme is derived, which explicitly depends on the Planck constant. This conclusion implies the semiclassical regime's admissible meshing strategies for obtaining "correct" physical observables. |
| title | Strong Approximation of Stochastic Semiclassical Schroedinger Equation with Multiplicative Noise |
| topic | Numerical Analysis |
| url | https://arxiv.org/abs/2408.09118 |