Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial
On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an
essential layer of safety assurance that could lead to more principled decision making by …
essential layer of safety assurance that could lead to more principled decision making by …
Assessment of punching shear strength of FRP-RC slab-column connections using machine learning algorithms
Recently, the use of fiber-reinforced polymer (FRP) bars replacing steel reinforcement has
been widely applied to overcome the corrosion issue, particularly concrete slab-column …
been widely applied to overcome the corrosion issue, particularly concrete slab-column …
Multi-objective optimization of explosive waste treatment process considering environment via Bayesian active learning
A fluidized bed is a next-generation explosive waste treatment reactor that is safer and emits
less pollutants (eg, NOx) than a rotary kiln. When a fluidized bed reactor is used to treat …
less pollutants (eg, NOx) than a rotary kiln. When a fluidized bed reactor is used to treat …
Practical metamodel-assisted multi-objective design optimization for improved sustainability and buildability of wind turbine foundations
A Mathern, V Penadés-Plà, J Armesto Barros… - Structural and …, 2022 - Springer
In this work, we study the potential of using kriging metamodelling to perform multi-objective
structural design optimization using finite element analysis software and design standards …
structural design optimization using finite element analysis software and design standards …
Multi-fidelity Bayesian Optimization in Engineering Design
Resided at the intersection of multi-fidelity optimization (MFO) and Bayesian optimization
(BO), MF BO has found a niche in solving expensive engineering design optimization …
(BO), MF BO has found a niche in solving expensive engineering design optimization …
A radial-basis function mesh morphing and Bayesian optimization framework for vehicle crashworthiness design
The vehicular structural system design is critical to protect passengers from fatal injuries in
inevitable accidents. Traditional optimization methods take only metal sheet thickness, ie …
inevitable accidents. Traditional optimization methods take only metal sheet thickness, ie …
Constrained causal Bayesian optimization
We propose constrained causal Bayesian optimization (cCBO), an approach for finding
interventions in a known causal graph that optimize a target variable under some …
interventions in a known causal graph that optimize a target variable under some …
Bayesian optimization for robust design of steel frames with joint and individual probabilistic constraints
This work proposes a Bayesian optimization (BO) method for solving multi-objective robust
design optimization (RDO) problems of steel frames under aleatory uncertainty in external …
design optimization (RDO) problems of steel frames under aleatory uncertainty in external …
[HTML][HTML] A multifidelity Bayesian optimization method for inertial confinement fusion design
Due to their cost, experiments for inertial confinement fusion (ICF) heavily rely on numerical
simulations to guide design. As simulation technology progresses, so too can the fidelity of …
simulations to guide design. As simulation technology progresses, so too can the fidelity of …
Optimization of process parameters in additive manufacturing based on the finite element method
J Wang, P Papadopoulos - arXiv preprint arXiv:2310.15525, 2023 - arxiv.org
A design optimization framework for process parameters of additive manufacturing based on
finite element simulation is proposed. The finite element method uses a coupled …
finite element simulation is proposed. The finite element method uses a coupled …