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 …
Friction stir based welding, processing, extrusion and additive manufacturing
Friction stir welding and processing enabled the creation of stronger joints, novel ultrafine-
grained metals, new metal matrix composites, and multifunctional surfaces at user-defined …
grained metals, new metal matrix composites, and multifunctional surfaces at user-defined …
Expected improvement for expensive optimization: a review
The expected improvement (EI) algorithm is a very popular method for expensive
optimization problems. In the past twenty years, the EI criterion has been extended to deal …
optimization problems. In the past twenty years, the EI criterion has been extended to deal …
A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design
Metamodeling is becoming a rather popular means to approximate the expensive
simulations in today's complex engineering design problems since accurate metamodels …
simulations in today's complex engineering design problems since accurate metamodels …
Review of multi-fidelity models
MG Fernández-Godino - arXiv preprint arXiv:1609.07196, 2016 - arxiv.org
This article provides an overview of multi-fidelity modeling trends. Fidelity in modeling refers
to the level of detail and accuracy provided by a predictive model or simulation. Generally …
to the level of detail and accuracy provided by a predictive model or simulation. Generally …
Transfer learning based multi-fidelity physics informed deep neural network
S Chakraborty - Journal of Computational Physics, 2021 - Elsevier
For many systems in science and engineering, the governing differential equation is either
not known or known in an approximate sense. Analyses and design of such systems are …
not known or known in an approximate sense. Analyses and design of such systems are …
A multi-fidelity surrogate model based on extreme support vector regression: Fusing different fidelity data for engineering design
Purpose Extreme support vector regression (ESVR) has been widely used in the design,
analysis and optimization of engineering systems of its fast training speed and good …
analysis and optimization of engineering systems of its fast training speed and good …
Remarks on multi-output Gaussian process regression
Multi-output regression problems have extensively arisen in modern engineering
community. This article investigates the state-of-the-art multi-output Gaussian processes …
community. This article investigates the state-of-the-art multi-output Gaussian processes …
Issues in deciding whether to use multifidelity surrogates
Multifidelity surrogates are essential in cases where it is not affordable to have more than a
few high-fidelity samples, but it is affordable to have as many low-fidelity samples as …
few high-fidelity samples, but it is affordable to have as many low-fidelity samples as …
Modern Monte Carlo methods for efficient uncertainty quantification and propagation: A survey
J Zhang - Wiley Interdisciplinary Reviews: Computational …, 2021 - Wiley Online Library
Uncertainty quantification (UQ) includes the characterization, integration, and propagation of
uncertainties that result from stochastic variations and a lack of knowledge or data in the …
uncertainties that result from stochastic variations and a lack of knowledge or data in the …