Results 121 to 130 of about 56,902 (196)

Temperature prediction based on a space-time regression-kriging model. [PDF]

J Appl Stat, 2020
Li S, Griffith DA, Shu H.
europepmc   +1 more source

Coarse‐to‐Fine Spatial Modeling: A Scalable, Machine‐Learning‐Compatible Framework

Geographical Analysis, Volume 58, Issue 2, April 2026.
ABSTRACT This study proposes coarse‐to‐fine spatial modeling (CFSM) as a scalable and machine learning‐compatible alternative to conventional spatial process models. Unlike conventional covariance‐based spatial models, CFSM represents spatial processes using a multiscale ensemble of local models.
Daisuke Murakami, Alexis Comber, Takahiro Yoshida, Narumasa Tsutsumida, Chris Brunsdon, Tomoki Nakaya   +5 more
wiley   +1 more source

Research on Multi-Alternatives Problem of Finite Element Model Updating Based on IAFSA and Kriging Model. [PDF]

Sensors (Basel), 2020
Kang J, Zhang X, Cao H, Qin S.
europepmc   +1 more source

Focal‐Feature Regression Kriging

Geographical Analysis, Volume 58, Issue 2, April 2026.
ABSTRACT Spatial interpolation is a crucial task in geography. As perhaps the most widely used interpolation methods, geostatistical models‐such as Ordinary Kriging (OK)‐assume spatial stationarity, which makes it difficult to capture the nonstationary characteristics of geographic variables.
Peng Luo, Yilong Wu, Yongze Song
wiley   +1 more source

Reliability Analysis of Subsea Oil and Gas Pipelines with Corrosion Defects Under Impact Load

Shiyou jixie
Subsea pipelines are “lifelines” of offshore oil and gas transportation， and their reliability directly affects the safety of underwater oil and gas exploitation. Considering that the in-service subsea oil and gas pipelines are subjected to the corrosion
Zeng Wei, Tian Xumin, Zheng Jilin, Zhu Dongrun   +3 more
doaj   +1 more source

Metamodel-based importance sampling for structural reliability analysis

, 2011
Structural reliability methods aim at computing the probability of failure of systems with respect to some prescribed performance functions. In modern engineering such functions usually resort to running an expensive-to-evaluate computational model (e.g.
Deheeger, F., Dubourg, V., Sudret, B.
core   +1 more source

Financial Time Series Uncertainty: A Review of Probabilistic AI Applications

Journal of Economic Surveys, Volume 40, Issue 2, Page 915-953, April 2026.
ABSTRACT Probabilistic machine learning models offer a distinct advantage over traditional deterministic approaches by quantifying both epistemic uncertainty (stemming from limited data or model knowledge) and aleatoric uncertainty (due to inherent randomness in the data), along with full distributional forecasts.
Sivert Eggen, Tord Johan Espe, Kristoffer Grude, Morten Risstad, Rickard Sandberg   +4 more
wiley   +1 more source

Efficient approximating the most-probable-point trajectory method for time-variant reliability analysis

Hangkong gongcheng jinzhan
When solving complex time-variant reliability analysis（TRA）problems，the traditional TRA methods have the problem of low solving efficiency. Based on the approximating most-probable-points trajectory （AMPPT）method，the efficient approximating the most ...
ZOU Nanzheng, GONG Chunlin, ZHANG Yunwei, MA Mengying, DU Siyi, LI Chunna   +5 more
doaj   +1 more source

Strong genetic differentiation but limited niche partitioning in a sympatric species pair separated by an allochronic reproductive barrier

Systematic Entomology, Volume 51, Issue 2, April‐June 2026.
Sympatric sister species demonstrate substantial geographic and host overlap, with ecological analyses only finding subtle microecological variation in habitat use. Molecular analyses reveal clear genetic differentiation with no evidence of hybridization, suggesting strong reproductive isolation between species. Differences in time‐of‐day mating appear
Mitchell Irvine, Zachary Stewart, Nagalingam Kumaran, Chapa G. Manawaduge, James Ryan, Solomon Balagawi, Brendan Missenden, Melissa Starkie, Anthony R. Clarke, David Hurwood, Peter Prentis   +10 more
wiley   +1 more source

A Coupled Adaptive Kriging Model and Generalized Subset Simulation Hybrid Reliability Analysis Method for Rare Failure Events

IEEE Access
This research proposes a novel hybrid reliability analysis method for rare failure events, which integrates the coupled Adaptive Kriging model and Generalized Subset Simulation (AK-GSS).
Yunhan Ling, Huajun Peng, Yong Sun, Chao Yuan, Zining Su, Xiaoxiao Tian, Peng Nie, Hengfei Yang, Shiyuan Yang   +8 more
doaj   +1 more source