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   Articles with "multifidelity" as a keyword



MFNets: data efficient all-at-once learning of multifidelity surrogates as directed networks of information sources

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recommendations! Published in 2021 at "Computational Mechanics"

DOI: 10.1007/s00466-021-02042-0

Abstract: We present an approach for constructing a surrogate from ensembles of information sources of varying cost and accuracy. The multifidelity surrogate encodes connections between information sources as a directed acyclic graph, and is trained via… read more here.

Keywords: information; information sources; multifidelity; data efficient ... See more keywords

Efficient prediction of turbulent flow quantities using a Bayesian hierarchical multifidelity model

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recommendations! Published in 2022 at "Journal of Fluid Mechanics"

DOI: 10.1017/jfm.2023.327

Abstract: Abstract Multifidelity models (MFMs) can be used to construct predictive models for flow quantities of interest (QoIs) over the space of uncertain/design parameters, with the purpose of uncertainty quantification, data fusion and optimization. For numerical… read more here.

Keywords: turbulent; turbulent flow; flow quantities; fidelity ... See more keywords
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Prediction of Frequency-Dependent Optical Spectrum for Solid Materials: A Multioutput and Multifidelity Machine Learning Approach.

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recommendations! Published in 2024 at "ACS applied materials & interfaces"

DOI: 10.1021/acsami.4c07328

Abstract: The frequency-dependent optical spectrum is pivotal for a broad range of applications from material characterization to optoelectronics and energy harvesting. Data-driven surrogate models, trained on density functional theory (DFT) data, have effectively alleviated the scalability… read more here.

Keywords: optical spectrum; dependent optical; multifidelity; frequency dependent ... See more keywords

Multifidelity Information Fusion with Machine Learning: A Case Study of Dopant Formation Energies in Hafnia.

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recommendations! Published in 2019 at "ACS applied materials & interfaces"

DOI: 10.1021/acsami.9b02174

Abstract: Cost versus accuracy trade-offs are frequently encountered in materials science and engineering, where a particular property of interest can be measured/computed at different levels of accuracy or fidelity. Naturally, the most accurate measurement is also… read more here.

Keywords: information; machine learning; fidelity; multifidelity ... See more keywords

Tempered multifidelity importance sampling for gravitational wave parameter estimation

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recommendations! Published in 2024 at "Physical Review D"

DOI: 10.1103/physrevd.110.104037

Abstract: Estimating the parameters of compact binaries which coalesce and produce gravitational waves is a challenging Bayesian inverse problem. Gravitational-wave parameter estimation lies within the class of multifidelity problems, where a variety of models with differing… read more here.

Keywords: gravitational wave; parameter estimation; multifidelity; importance sampling ... See more keywords

Out-of-Distribution Domain Exploration by a Multifidelity Deep Learning Model to Estimate Electromagnetic Responses of Metasurfaces

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recommendations! Published in 2024 at "IEEE Transactions on Antennas and Propagation"

DOI: 10.1109/tap.2024.3426290

Abstract: The multifidelity approach is a promising way to efficiently train the neural network which can be used to estimate the electromagnetic (EM) responses of metasurfaces. Empirical formulas or coarse-meshed full-wave simulation (FS) are two widely… read more here.

Keywords: estimate; multifidelity; estimate electromagnetic; electromagnetic responses ... See more keywords

Data-driven constitutive meta-modeling of nonlinear rheology via multifidelity neural networks

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recommendations! Published in 2024 at "Journal of Rheology"

DOI: 10.1122/8.0000831

Abstract: Predicting the response of complex fluids to different flow conditions has been the focal point of rheology and is generally done via constitutive relations. There are, nonetheless, scenarios in which not much is known from… read more here.

Keywords: rheology; meta modeling; multifidelity; material response ... See more keywords

Multifidelity Genetic Transfer: An Efficient Framework for Production Optimization

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recommendations! Published in 2021 at "Spe Journal"

DOI: 10.2118/205013-pa

Abstract: Production optimization led by computing intelligence can greatly improve oilfield economic effectiveness. However, it is confronted with huge computational challenge because of the expensive black-box objective function and the high-dimensional design variables. Many low-fidelity methods… read more here.

Keywords: production optimization; transfer; fidelity; framework ... See more keywords

Debiased Multifidelity Approach to Surrogate Modeling in Aerospace Applications

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recommendations! Published in 2025 at "Journal of Aircraft"

DOI: 10.2514/1.c037765

Abstract: We propose a multifidelity formulation for generating cokriging surrogates of complex physics models. First, we show that the standard autoregressive recursive approach may be subject to substantial limitations due to possible modeler’s biases/errors. These are… read more here.

Keywords: debiased multifidelity; multifidelity; aerospace applications; approach ... See more keywords

Optimization of Information Gain in Multifidelity High-Speed Pressure Predictions

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recommendations! Published in 2021 at "AIAA Journal"

DOI: 10.2514/1.j059507

Abstract: The objective of multifidelity modeling is to achieve both accurate and efficient predictions by combining high- and low-fidelity models. A flexible approach considering additive, multiplicative, a... read more here.

Keywords: gain multifidelity; information gain; optimization information; multifidelity high ... See more keywords

Multifidelity Methodology for Reduced-Order Models with High-Dimensional Inputs

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recommendations! Published in 2024 at "AIAA Journal"

DOI: 10.2514/1.j064110

Abstract: In the early stages of aerospace design, reduced-order models (ROMs) are crucial for minimizing computational costs associated with using physics-rich field information in many-query scenarios requiring multiple evaluations. The intricacy of aerospace design demands the… read more here.

Keywords: order models; methodology; reduced order; high dimensional ... See more keywords