Space-time covariance structures and models
In recent years, interest has grown in modeling spatio-temporal data generated from
monitoring networks, satellite imaging, and climate models. Under Gaussianity, the …
monitoring networks, satellite imaging, and climate models. Under Gaussianity, the …
A review of information field theory for Bayesian inference of random fields
Several physical problems require Bayesian inference of spatial, or spatio-temporal
phenomenon–often modeled as random fields defined on a continuous domain–from a …
phenomenon–often modeled as random fields defined on a continuous domain–from a …
Big influence of small random imperfections in origami-based metamaterials
Origami structures demonstrate great theoretical potential for creating metamaterials with
exotic properties. However, there is a lack of understanding of how imperfections influence …
exotic properties. However, there is a lack of understanding of how imperfections influence …
Seismic demand and capacity models, and fragility estimates for underground structures considering spatially varying soil properties
This study proposes probabilistic seismic demand and capacity models for underground
structures considering the spatial distribution characteristics of soil properties. The proposed …
structures considering the spatial distribution characteristics of soil properties. The proposed …
Data-driven simulation of two-dimensional cross-correlated random fields from limited measurements using joint sparse representation
Cross-correlated random fields are an essential tool for simultaneously modeling both auto-
and cross-correlation structures of spatial or temporal quantities in stochastic analysis of …
and cross-correlation structures of spatial or temporal quantities in stochastic analysis of …
A sparse data-driven stochastic damage model for seismic reliability assessment of reinforced concrete structures
It is important to study the seismic reliability of concrete structures based on real measured
data of the material properties. The data of material properties collected in practice is usually …
data of the material properties. The data of material properties collected in practice is usually …
New Kriging methods for efficient system slope reliability analysis considering soil spatial variability
SY Huang, LL Liu - Reliability Engineering & System Safety, 2024 - Elsevier
Recently, multiple Kriging (MK) metamodels have demonstrated their advantages in system
slope reliability analysis. However, the inherent spatial variability of soil properties has not …
slope reliability analysis. However, the inherent spatial variability of soil properties has not …
Spatio-temporal DeepKriging for interpolation and probabilistic forecasting
Gaussian processes (GP) and Kriging are widely used in traditional spatio-temporal
modelling and prediction. These techniques typically presuppose that the data are observed …
modelling and prediction. These techniques typically presuppose that the data are observed …
Multi-level, multi-variate, non-stationary, random field modeling and fragility analysis of engineering systems
Engineering systems can often be represented considering models at multiple levels.
Different properties within each level are typically inhomogeneous in space and cross …
Different properties within each level are typically inhomogeneous in space and cross …
Physics-based fragility functions: Their mathematical formulation and use in the reliability and resilience analysis of transportation infrastructure
Transportation infrastructure provide vital services that support and enable societal
functions. Therefore, ensuring their reliability and resilience is of critical importance. The …
functions. Therefore, ensuring their reliability and resilience is of critical importance. The …