State-of-the-art and comparative review of adaptive sampling methods for kriging
Metamodels aim to approximate characteristics of functions or systems from the knowledge
extracted on only a finite number of samples. In recent years kriging has emerged as a …
extracted on only a finite number of samples. In recent years kriging has emerged as a …
Managing computational complexity using surrogate models: a critical review
In simulation-based realization of complex systems, we are forced to address the issue of
computational complexity. One critical issue that must be addressed is the approximation of …
computational complexity. One critical issue that must be addressed is the approximation of …
Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems
Deep learning has been shown to be an effective tool in solving partial differential equations
(PDEs) through physics-informed neural networks (PINNs). PINNs embed the PDE residual …
(PDEs) through physics-informed neural networks (PINNs). PINNs embed the PDE residual …
A Kriging-based decoupled non-probability reliability-based design optimization scheme for piezoelectric PID control systems
L Wang, Y Zhao, J Liu - Mechanical Systems and Signal Processing, 2023 - Elsevier
When dealing with optimization problems, the introduction of uncertainty will greatly
increase the difficulty of solving the problem. The traditional reliability-based design …
increase the difficulty of solving the problem. The traditional reliability-based design …
On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling
Data-driven constitutive modeling is an emerging field in computational solid mechanics
with the prospect of significantly relieving the computational costs of hierarchical …
with the prospect of significantly relieving the computational costs of hierarchical …
Applying Bayesian optimization with Gaussian process regression to computational fluid dynamics problems
Y Morita, S Rezaeiravesh, N Tabatabaei… - Journal of …, 2022 - Elsevier
Bayesian optimization (BO) based on Gaussian process regression (GPR) is applied to
different CFD (computational fluid dynamics) problems which can be of practical relevance …
different CFD (computational fluid dynamics) problems which can be of practical relevance …
Local approximate Gaussian process regression for data-driven constitutive models: development and comparison with neural networks
Hierarchical computational methods for multiscale mechanics such as the FE 2 and FE-FFT
methods are generally accompanied by high computational costs. Data-driven approaches …
methods are generally accompanied by high computational costs. Data-driven approaches …
Scalable gradient–enhanced artificial neural networks for airfoil shape design in the subsonic and transonic regimes
MA Bouhlel, S He, JRRA Martins - Structural and Multidisciplinary …, 2020 - Springer
Airfoil shape design is one of the most fundamental elements in aircraft design. Existing
airfoil design tools require at least a few minutes to analyze a new shape and hours to …
airfoil design tools require at least a few minutes to analyze a new shape and hours to …
Reliability index function approximation based on adaptive double-loop Kriging for reliability-based design optimization
X Zhang, Z Lu, K Cheng - Reliability Engineering & System Safety, 2021 - Elsevier
Reliability-based design optimization (RBDO) aims at minimizing general cost under the
reliability constraints by considering the inherent uncertainties in engineering. In this work …
reliability constraints by considering the inherent uncertainties in engineering. In this work …
Optimal feedback law recovery by gradient-augmented sparse polynomial regression
A sparse regression approach for the computation of high-dimensional optimal feedback
laws arising in deterministic nonlinear control is proposed. The approach exploits the control …
laws arising in deterministic nonlinear control is proposed. The approach exploits the control …