[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 …
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 …
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 …
Hierarchical surrogate modeling with multiple order partially observed information
Understanding the input and output relationship of a complex engineering system is an
essential task that attracts widespread interests in engineering design fields. To investigate …
essential task that attracts widespread interests in engineering design fields. To investigate …
Efficient and robust optimal design for quantile regression based on linear programming
When informing decisions with experimental data, it is often necessary to quantify the
distribution tails of uncertain system responses using limited data. To maximize the …
distribution tails of uncertain system responses using limited data. To maximize the …
Risk averse constrained blackbox optimization under mixed aleatory/epistemic uncertainties
This paper addresses risk averse constrained optimization problems where the objective
and constraint functions can only be computed by a blackbox subject to unknown …
and constraint functions can only be computed by a blackbox subject to unknown …
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-Based Optimization of Offshore Salt Caverns for CO2 Abatement
In recent years, projects have been proposed to utilize salt caverns as a storage method for
supercritical CO2 (s-CO2) and have been carried out around the world, which can effectively …
supercritical CO2 (s-CO2) and have been carried out around the world, which can effectively …
Peregrination through blackbox optimization: multimodality, stochasticity and risk aversion.
R Couderc - 2023 - theses.hal.science
To tackle blackbox optimization, this thesis consists of three contributions, designed around
a single notion: Gaussian exploration of space. This exploration consists of sampling points …
a single notion: Gaussian exploration of space. This exploration consists of sampling points …
Reliability Analysis Using Multi-Fidelity Physics-Informed Machine Learning with Partially Observed Information
K Babski-Reeves, B Eksioglu, D Hampton - search.proquest.com
Reliability analysis of complex engineering systems requires repeated testing to determine
system performance and underlying failure mechanisms with respect to a number of …
system performance and underlying failure mechanisms with respect to a number of …