Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges
The computerized simulations of physical and socio-economic systems have proliferated in
the past decade, at the same time, the capability to develop high-fidelity system predictive …
the past decade, at the same time, the capability to develop high-fidelity system predictive …
Hall-effect sensor design with physics-informed Gaussian process modeling
Magnetic field sensor devices have been widely used to track changes in magnetic flux
concentration, and the Hall sensors are promising in many engineering applications. Design …
concentration, and the Hall sensors are promising in many engineering applications. Design …
Adaptive surrogate models for uncertainty quantification with partially observed information
View Video Presentation: https://doi. org/10.2514/6.2022-1439. vid Surrogate models are
commonly used to reduce computational cost by replacing expensive physical models with …
commonly used to reduce computational cost by replacing expensive physical models with …
An enhanced squared exponential kernel with Manhattan similarity measure for high dimensional Gaussian process models
Abstract The Gaussian Process (GP) model has become one of the most popular methods
and exhibits superior performance among surrogate models in many engineering design …
and exhibits superior performance among surrogate models in many engineering design …
Hall Effect Sensor Design Optimization With Multi-Physics Informed Gaussian Process Modeling
Magnetic field sensor devices have been widely used to track changes in magnetic flux
concentration, and the Hall sensors are promising in many engineering applications. Design …
concentration, and the Hall sensors are promising in many engineering applications. Design …
Sequential sampling based reliability analysis for high dimensional rare events with confidence intervals
Abstract Analysis of rare failure events accurately is often challenging with an affordable
computational cost in many engineering applications, and this is especially true for problems …
computational cost in many engineering applications, and this is especially true for problems …
Sequential sampling based asymptotic probability estimation for high dimensional rare events
Accurate analysis of rare failure events with an affordable computational cost is often
challenging in many engineering applications, particularly for problems with high …
challenging in many engineering applications, particularly for problems with high …
Physics-constrained machine learning for reliability-based design optimization
Summary & ConclusionsTo aid and improve the reliability of product designs, repeated
safety tests are required to find out the safety performance of the product with respect to …
safety tests are required to find out the safety performance of the product with respect to …
Reliability analysis with partially observed information
This paper presents a new method for reliability analysis with partially observed information,
which integrates the Bayesian Gaussian process latent variable model (BGP-LVM) with …
which integrates the Bayesian Gaussian process latent variable model (BGP-LVM) with …