All-at-once approach to multifidelity polynomial chaos expansion surrogate modeling
Sign Up to like & getrecommendations! Published in 2017 at "Aerospace Science and Technology"
DOI: 10.1016/j.ast.2017.07.043
Abstract: Abstract A new approach to multifidelity, gradient-enhanced surrogate modeling using polynomial chaos expansions is presented. This approach seeks complementary additive and multiplicative corrections to low-fidelity data whereas current hybrid methods in the literature attempt to… read more here.
Keywords: approach multifidelity; surrogate; polynomial chaos; approach ... See more keywords
Surrogate modeling of elasto-plastic problems via long short-term memory neural networks and proper orthogonal decomposition
Sign Up to like & getrecommendations! Published in 2021 at "Computer Methods in Applied Mechanics and Engineering"
DOI: 10.1016/j.cma.2021.114030
Abstract: Abstract Because of its nonlinearity and path-dependency, analysis of the elasto-plastic behavior of the finite element (FE) model is computationally expensive. By directly learning sequential data, modeling plasticity via deep learning has shown successful performance… read more here.
Keywords: plastic; surrogate modeling; long short; pod ... See more keywords
Seismic fragility analysis of a coupled tank-piping system based on artificial ground motions and surrogate modeling
Sign Up to like & getrecommendations! Published in 2021 at "Journal of Loss Prevention in The Process Industries"
DOI: 10.1016/j.jlp.2021.104575
Abstract: Abstract The catastrophic consequences of recent NaTech events triggered by earthquakes highlighted the inadequacy of standard approaches to seismic risk assessment of chemical process plants. To date, the risk assessment of such facilities mainly relies… read more here.
Keywords: system; ground; surrogate modeling; process ... See more keywords
A multi-fidelity shape optimization via surrogate modeling for civil structures
Sign Up to like & getrecommendations! Published in 2018 at "Journal of Wind Engineering and Industrial Aerodynamics"
DOI: 10.1016/j.jweia.2018.04.022
Abstract: Abstract Shape optimization serves as a powerful tool to reduce wind effects on buildings. Past studies have demonstrated the superiority of the shape tailoring technique in aerodynamic mitigation through recessing or chamfering building corners, etc.… read more here.
Keywords: shape optimization; fidelity; surrogate; surrogate modeling ... See more keywords
Surrogate Modeling of Fugacity Coefficients Using Adaptive Sampling
Sign Up to like & getrecommendations! Published in 2019 at "Industrial & Engineering Chemistry Research"
DOI: 10.1021/acs.iecr.9b02758
Abstract: Complex thermodynamic models such as the perturbed chain statistical associating fluid theory (PC-SAFT) model describe the phase equilibria in a chemical process in a very precise way; however, because of their implicit and complex nature,… read more here.
Keywords: adaptive sampling; modeling fugacity; fugacity; fugacity coefficients ... See more keywords
Improving E3SM Land Model Photosynthesis Parameterization via Satellite SIF, Machine Learning, and Surrogate Modeling
Sign Up to like & getrecommendations! Published in 2023 at "Journal of Advances in Modeling Earth Systems"
DOI: 10.1029/2022ms003135
Abstract: The parameterization of key photosynthesis parameters is one of the key uncertain sources in modeling ecosystem gross primary productivity (GPP). Solar‐induced chlorophyll fluorescence (SIF) offers a good proxy for GPP since it marks the actual… read more here.
Keywords: satellite sif; sif; surrogate modeling; gpp ... See more keywords
Rapid Design of 3D Reflectarray Antennas by Inverse Surrogate Modeling and Regularization
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Access"
DOI: 10.1109/access.2023.3254204
Abstract: Reflectarrays (RAs) exhibit important advantages over conventional antenna arrays, especially in terms of realizing pencil-beam patterns without the employment of the feeding networks. Unfortunately, microstrip RA implementations feature narrow bandwidths, and are severely affected by… read more here.
Keywords: rapid design; regularization; surrogate modeling; inverse surrogate ... See more keywords
Sequential Online Dispatch in Design of Experiments for Single- and Multiple-Response Surrogate Modeling
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Transactions on Automation Science and Engineering"
DOI: 10.1109/tase.2020.2969884
Abstract: As parallel computing becomes increasingly important in many real-world applications, a batch sequential experimental design (BSED), which adds a batch of computer experiments per iteration and runs these simulations in parallel, is gaining popularity in… read more here.
Keywords: online dispatch; surrogate modeling; surrogate; sequential online ... See more keywords
A review of the artificial neural network surrogate modeling in aerodynamic design
Sign Up to like & getrecommendations! Published in 2019 at "Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering"
DOI: 10.1177/0954410019864485
Abstract: Artificial neural network surrogate modeling with its economic computational consumption and accurate generalization capabilities offers a feasible approach to aerodynamic design in the field of rapid investigation of design space and optimal solution searching. This… read more here.
Keywords: neural network; network; artificial neural; network surrogate ... See more keywords
Surrogate Modeling of Aerodynamic Simulations for Multiple Operating Conditions Using Machine Learning
Sign Up to like & getrecommendations! Published in 2018 at "AIAA Journal"
DOI: 10.2514/1.j056405
Abstract: This paper describes a methodology, called local decomposition method, which aims at building a surrogate model based on steady turbulent aerodynamic fields at multiple operating conditions. The various shapes taken by the aerodynamic fields due… read more here.
Keywords: methodology; machine learning; operating conditions; multiple operating ... See more keywords
Enhancing Computational Accuracy in Surrogate Modeling for Elastic–Plastic Problems by Coupling S-FEM and Physics-Informed Deep Learning
Sign Up to like & getrecommendations! Published in 2023 at "Mathematics"
DOI: 10.3390/math11092016
Abstract: Physics-informed neural networks (PINNs) provide a new approach to solving partial differential equations (PDEs), while the properties of coupled physical laws present potential in surrogate modeling. However, the accuracy of PINNs in solving forward problems… read more here.
Keywords: inverse problems; physics; surrogate modeling; coupling fem ... See more keywords