A Review of Recent Advances in Surrogate Models for Uncertainty Quantification of High-Dimensional Engineering Applications
Z Azarhoosh, MI Ghazaan - Computer Methods in Applied Mechanics and …, 2025 - Elsevier
In fields where predictions may have vital consequences, uncertainty quantification (UQ)
plays a crucial role, as it enables more accurate forecasts and mitigates the potential risks …
plays a crucial role, as it enables more accurate forecasts and mitigates the potential risks …
[HTML][HTML] Physics-informed machine learning for system reliability analysis and design with partially observed information
Constructing a high-fidelity predictive model is crucial for analyzing complex systems,
optimizing system design, and enhancing system reliability. Although Gaussian Process …
optimizing system design, and enhancing system reliability. Although Gaussian Process …
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 …
Multi-Task Learning for Design Under Uncertainty With Multi-Fidelity Partially Observed Information
The assessment of system performance and identification of failure mechanisms in complex
engineering systems often requires the use of computation-intensive finite element software …
engineering systems often requires the use of computation-intensive finite element software …
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 …
Multi-Task Multi-Fidelity Machine Learning for Reliability-Based Design With Partially Observed Information
In complex engineering systems, assessing system performance and underlying failure
mechanisms with respect to uncertain variables requires repeated testing, which is often …
mechanisms with respect to uncertain variables requires repeated testing, which is often …