Optimizing sequential experimental design with deep reinforcement learning
Bayesian approaches developed to solve the optimal design of sequential experiments are
mathematically elegant but computationally challenging. Recently, techniques using …
mathematically elegant but computationally challenging. Recently, techniques using …
Single and multi-objective real-time optimisation of an industrial injection moulding process via a Bayesian adaptive design of experiment approach
Minimising cycle time without inducing quality defects is a major challenge in injection
moulding (IM). Design of Experiment methods (DoE) have been widely studied for …
moulding (IM). Design of Experiment methods (DoE) have been widely studied for …
[HTML][HTML] Maximum a posteriori estimation for linear structural dynamics models using Bayesian optimization with rational polynomial chaos expansions
Bayesian analysis enables combining prior knowledge with measurement data to learn
model parameters. Commonly, one resorts to computing the maximum a posteriori (MAP) …
model parameters. Commonly, one resorts to computing the maximum a posteriori (MAP) …
Large-scale sandwich structures optimization using Bayesian method
H Liu, J Guo, J Wang, C Wang - International Journal of Mechanical …, 2024 - Elsevier
Benefiting from advanced features like high stiffness-to-weight ratios, sandwich structures
are widely used in aerospace for primary and secondary structures. As tasks grow more …
are widely used in aerospace for primary and secondary structures. As tasks grow more …
Cellular gradient algorithm for solving complex mechanical optimization design problems
R Wang, X Li, H Huang, Z Fan, F Huang… - International Journal of …, 2024 - Elsevier
In mechanical optimization design problems, there are often some non-continuous or non-
differentiable objective functions. For these non-continuous and non-differentiable …
differentiable objective functions. For these non-continuous and non-differentiable …
Inverse aerodynamic design of gas turbine blades using probabilistic machine learning
S Ghosh… - Journal of …, 2022 - asmedigitalcollection.asme.org
One of the critical components in industrial gas turbines (IGT) is the turbine blade. The
design of turbine blades needs to consider multiple aspects like aerodynamic efficiency …
design of turbine blades needs to consider multiple aspects like aerodynamic efficiency …
[HTML][HTML] Variational bayesian approximation of inverse problems using sparse precision matrices
Inverse problems involving partial differential equations (PDEs) are widely used in science
and engineering. Although such problems are generally ill-posed, different regularisation …
and engineering. Although such problems are generally ill-posed, different regularisation …
Bayesian learning of orthogonal embeddings for multi-fidelity Gaussian Processes
Uncertainty propagation in complex engineering systems often poses significant
computational challenges related to modeling and quantifying probability distributions of …
computational challenges related to modeling and quantifying probability distributions of …
Multi-objective Bayesian optimization of fused filament fabrication parameters for enhanced specific fracture energy in PLA-carbon fiber composites
Short carbon fiber-reinforced polymers (SCFRPs) are used in automotive and aerospace
applications because of their superior strength-to-weight ratio and resistance to fatigue and …
applications because of their superior strength-to-weight ratio and resistance to fatigue and …
An active learning framework for the rapid assessment of galvanic corrosion
The current present in a galvanic couple can define its resistance or susceptibility to
corrosion. However, as the current is dependent upon environmental, material, and …
corrosion. However, as the current is dependent upon environmental, material, and …