127 related articles for article (PubMed ID: 37849188)
1. Kernel-based learning framework for discovering the governing equations of stochastic jump-diffusion processes directly from data.
Sun W; Feng J; Su J; Guo Q
Phys Rev E; 2023 Sep; 108(3-2):035306. PubMed ID: 37849188
[TBL] [Abstract][Full Text] [Related]
2. Extracting stochastic governing laws by non-local Kramers-Moyal formulae.
Lu Y; Li Y; Duan J
Philos Trans A Math Phys Eng Sci; 2022 Aug; 380(2229):20210195. PubMed ID: 35719068
[TBL] [Abstract][Full Text] [Related]
3. Arbitrary-Order Finite-Time Corrections for the Kramers-Moyal Operator.
Rydin Gorjão L; Witthaut D; Lehnertz K; Lind PG
Entropy (Basel); 2021 Apr; 23(5):. PubMed ID: 33923154
[TBL] [Abstract][Full Text] [Related]
4. Detecting the maximum likelihood transition path from data of stochastic dynamical systems.
Dai M; Gao T; Lu Y; Zheng Y; Duan J
Chaos; 2020 Nov; 30(11):113124. PubMed ID: 33261328
[TBL] [Abstract][Full Text] [Related]
5. Analysis and data-driven reconstruction of bivariate jump-diffusion processes.
Rydin Gorjão L; Heysel J; Lehnertz K; Tabar MRR
Phys Rev E; 2019 Dec; 100(6-1):062127. PubMed ID: 31962437
[TBL] [Abstract][Full Text] [Related]
6. Testing Jump-Diffusion in Epileptic Brain Dynamics: Impact of Daily Rhythms.
Kurth JG; Rings T; Lehnertz K
Entropy (Basel); 2021 Mar; 23(3):. PubMed ID: 33807933
[TBL] [Abstract][Full Text] [Related]
7. Sparse learning of stochastic dynamical equations.
Boninsegna L; Nüske F; Clementi C
J Chem Phys; 2018 Jun; 148(24):241723. PubMed ID: 29960307
[TBL] [Abstract][Full Text] [Related]
8. An end-to-end deep learning approach for extracting stochastic dynamical systems with α-stable Lévy noise.
Fang C; Lu Y; Gao T; Duan J
Chaos; 2022 Jun; 32(6):063112. PubMed ID: 35778145
[TBL] [Abstract][Full Text] [Related]
9. Discovering transition phenomena from data of stochastic dynamical systems with Lévy noise.
Lu Y; Duan J
Chaos; 2020 Sep; 30(9):093110. PubMed ID: 33003930
[TBL] [Abstract][Full Text] [Related]
10. Geometric and projection effects in Kramers-Moyal analysis.
Lade SJ
Phys Rev E Stat Nonlin Soft Matter Phys; 2009 Sep; 80(3 Pt 1):031137. PubMed ID: 19905092
[TBL] [Abstract][Full Text] [Related]
11. Discovering governing equations from data by sparse identification of nonlinear dynamical systems.
Brunton SL; Proctor JL; Kutz JN
Proc Natl Acad Sci U S A; 2016 Apr; 113(15):3932-7. PubMed ID: 27035946
[TBL] [Abstract][Full Text] [Related]
12. Finding nonlinear system equations and complex network structures from data: A sparse optimization approach.
Lai YC
Chaos; 2021 Aug; 31(8):082101. PubMed ID: 34470223
[TBL] [Abstract][Full Text] [Related]
13. Master equations and the theory of stochastic path integrals.
Weber MF; Frey E
Rep Prog Phys; 2017 Apr; 80(4):046601. PubMed ID: 28306551
[TBL] [Abstract][Full Text] [Related]
14. Data-driven discovery of the governing equations of dynamical systems via moving horizon optimization.
Lejarza F; Baldea M
Sci Rep; 2022 Jul; 12(1):11836. PubMed ID: 35821394
[TBL] [Abstract][Full Text] [Related]
15. Analytical Derivation of Moment Equations in Stochastic Chemical Kinetics.
Sotiropoulos V; Kaznessis YN
Chem Eng Sci; 2011 Feb; 66(3):268-277. PubMed ID: 21949443
[TBL] [Abstract][Full Text] [Related]
16. Robust data-driven discovery of governing physical laws with error bars.
Zhang S; Lin G
Proc Math Phys Eng Sci; 2018 Sep; 474(2217):20180305. PubMed ID: 30333709
[TBL] [Abstract][Full Text] [Related]
17. Data-driven discovery of partial differential equations.
Rudy SH; Brunton SL; Proctor JL; Kutz JN
Sci Adv; 2017 Apr; 3(4):e1602614. PubMed ID: 28508044
[TBL] [Abstract][Full Text] [Related]
18. Sparse inference and active learning of stochastic differential equations from data.
Huang Y; Mabrouk Y; Gompper G; Sabass B
Sci Rep; 2022 Dec; 12(1):21691. PubMed ID: 36522347
[TBL] [Abstract][Full Text] [Related]
19. Convolutionless Nakajima-Zwanzig equations for stochastic analysis in nonlinear dynamical systems.
Venturi D; Karniadakis GE
Proc Math Phys Eng Sci; 2014 Jun; 470(2166):20130754. PubMed ID: 24910519
[TBL] [Abstract][Full Text] [Related]
20. Data-driven discovery of the governing equations for transport in heterogeneous media by symbolic regression and stochastic optimization.
Im J; de Barros FPJ; Masri S; Sahimi M; Ziff RM
Phys Rev E; 2023 Jan; 107(1):L013301. PubMed ID: 36797859
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
