Zobrazeno 1 - 10
of 34
pro vyhledávání: '"Xin T Tong"'
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-12 (2023)
Abstract Nature-inspired swarm-based algorithms are increasingly applied to tackle high-dimensional and complex optimization problems across disciplines. They are general purpose optimization algorithms, easy to implement and assumption-free. Some co
Externí odkaz:
https://doaj.org/article/64a96450cfa3481d8d70982e323498f2
Publikováno v:
SIAM Journal on Numerical Analysis. 58:1263-1294
Ensemble Kalman inversion is a parallelizable methodology for solving inverse or parameter estimation problems. Although it is based on ideas from Kalman filtering, it may be viewed as a derivative-free optimization method. In its most basic form it
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::02b39eecfb2d8471110ed5f85bda941d
https://doi.org/10.1137/19m1242331
https://doi.org/10.1137/19m1242331
Publikováno v:
Inverse Problems
Inverse Problems, 2022, 38 (12), pp.124002. ⟨10.1088/1361-6420/ac9582⟩
Inverse Problems, 2022, 38 (12), pp.124002. ⟨10.1088/1361-6420/ac9582⟩
Markov chain Monte Carlo (MCMC) methods form one of the algorithmic foundations of Bayesian inverse problems. The recent development of likelihood-informed subspace (LIS) methods offers a viable route to designing efficient MCMC methods for exploring
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2371e447488b049cbea691211b93ef93
http://arxiv.org/abs/2202.00074
http://arxiv.org/abs/2202.00074
Autor:
Jana de Wiljes, Xin T. Tong
Concurrent observation technologies have made high-precision real-time data available in large quantities. Data assimilation (DA) is concerned with how to combine this data with physical models to produce accurate predictions. For spatial-temporal mo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1e0eeb045caabe57abe66984f1f40c8a
Publikováno v:
Electronic Journal of Statistics. 16
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::aba11ed6b81777325d5a48799a78220e
https://doi.org/10.1214/22-ejs2055
https://doi.org/10.1214/22-ejs2055
Ensemble Kalman inversion (EKI) is a derivative-free optimizer aimed at solving inverse problems, taking motivation from the celebrated ensemble Kalman filter. The purpose of this article is to consider the introduction of adaptive Tikhonov strategie
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0bb5156420d301e159165b5154fa107f
http://arxiv.org/abs/2110.09142
http://arxiv.org/abs/2110.09142
Publikováno v:
Chinese Annals of Mathematics, Series B. 40:891-924
Nonlinear dynamical stochastic models are ubiquitous in different areas. Their statistical properties are often of great interest, but are also very challenging to compute. Many excitable media models belong to such types of complex systems with larg
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e80a31a2f9a02c379eff8228ff14e520
https://doi.org/10.1007/s11401-019-0166-0
https://doi.org/10.1007/s11401-019-0166-0
Autor:
Andrew J. Majda, Xin T. Tong
Publikováno v:
Nonlinearity. 32:1641-1674
Extreme events and the heavy tail distributions driven by them are ubiquitous in various scientific, engineering and financial research. They are typically associated with stochastic instability caused by hidden unresolved processes. Previous studies
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::55bb5c6b5619df2a9cf85907c140c3de
https://doi.org/10.1088/1361-6544/aafbda
https://doi.org/10.1088/1361-6544/aafbda
Publikováno v:
arXiv
We investigate how ideas from covariance localization in numerical weather prediction can be used in Markov chain Monte Carlo (MCMC) sampling of high-dimensional posterior distributions arising in Bayesian inverse problems. To localize an inverse pro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7380181e580e7d41efa332a278e541ba
https://doi.org/10.1016/j.jcp.2018.12.008
https://doi.org/10.1016/j.jcp.2018.12.008
Publikováno v:
Research in the Mathematical Sciences. 8
Particle swarm optimization (PSO) is a member of nature-inspired metaheuristic algorithms. Its formulation is simple and does not need the computation of derivatives. It and its many variants have been applied to many different types of optimization
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6e3f3d3f2a6b39ab3315dce6e6b61022
https://doi.org/10.1007/s40687-020-00241-4
https://doi.org/10.1007/s40687-020-00241-4