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 …
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 …
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 …
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 …
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 …