Learning Stochastic Dynamics from Data
We present a noise guided trajectory based system identification method for inferring the dynamical structure from observation generated by stochastic differential equations. Our method can handle various kinds of noise, including the case when the the components of the noise is correlated. Our method can also learn both the noise level and drift term together from trajectory. We present various numerical tests for showcasing the superior performance of our learning algorithm..
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Preprint| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
arXiv.org - (2024) vom: 04. März Zur Gesamtaufnahme - year:2024 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Guo, Ziheng [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| Themen: |
000 |
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| PPN (Katalog-ID): |
XCH042753481 |
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| 245 | 1 | 0 | |a Learning Stochastic Dynamics from Data |
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| 520 | |a We present a noise guided trajectory based system identification method for inferring the dynamical structure from observation generated by stochastic differential equations. Our method can handle various kinds of noise, including the case when the the components of the noise is correlated. Our method can also learn both the noise level and drift term together from trajectory. We present various numerical tests for showcasing the superior performance of our learning algorithm. | ||
| 650 | 4 | |a Mathematics - Numerical Analysis |7 (dpeaa)DE-84 | |
| 650 | 4 | |a Computer Science - Numerical Analysis |7 (dpeaa)DE-84 | |
| 650 | 4 | |a 510 |7 (dpeaa)DE-84 | |
| 650 | 4 | |a 000 |7 (dpeaa)DE-84 | |
| 700 | 1 | |a Cialenco, Igor |4 aut | |
| 700 | 1 | |a Zhong, Ming |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t arXiv.org |g (2024) vom: 04. März |
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| 856 | 4 | 0 | |u https://arxiv.org/abs/2403.02595 |z kostenfrei |3 Volltext |
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