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Artykuły w czasopismach na temat „Multi-fidelity models”

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Data publikacji: 29 sierpnia 2023

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1

Razi, Mani, Robert M. Kirby, and Akil Narayan. "Fast predictive multi-fidelity prediction with models of quantized fidelity levels." Journal of Computational Physics 376 (January 2019): 992–1008. http://dx.doi.org/10.1016/j.jcp.2018.10.025.

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Perdikaris, P., M. Raissi, A. Damianou, N. D. Lawrence, and G. E. Karniadakis. "Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 473, no. 2198 (2017): 20160751. http://dx.doi.org/10.1098/rspa.2016.0751.

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Multi-fidelity modelling enables accurate inference of quantities of interest by synergistically combining realizations of low-cost/low-fidelity models with a small set of high-fidelity observations. This is particularly effective when the low- and high-fidelity models exhibit strong correlations, and can lead to significant computational gains over approaches that solely rely on high-fidelity models. However, in many cases of practical interest, low-fidelity models can only be well correlated to their high-fidelity counterparts for a specific range of input parameters, and potentially return

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Rumpfkeil, Markus P., Dean Bryson, and Phil Beran. "Multi-Fidelity Sparse Polynomial Chaos and Kriging Surrogate Models Applied to Analytical Benchmark Problems." Algorithms 15, no. 3 (2022): 101. http://dx.doi.org/10.3390/a15030101.

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In this article, multi-fidelity kriging and sparse polynomial chaos expansion (SPCE) surrogate models are constructed. In addition, a novel combination of the two surrogate approaches into a multi-fidelity SPCE-Kriging model will be presented. Accurate surrogate models, once obtained, can be employed for evaluating a large number of designs for uncertainty quantification, optimization, or design space exploration. Analytical benchmark problems are used to show that accurate multi-fidelity surrogate models can be obtained at lower computational cost than high-fidelity models. The benchmarks inc

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DiazDelaO, F. A., and S. Adhikari. "Bayesian assimilation of multi-fidelity finite element models." Computers & Structures 92-93 (February 2012): 206–15. http://dx.doi.org/10.1016/j.compstruc.2011.11.002.

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Rumpfkeil, Markus P., and Philip Beran. "Multi-fidelity surrogate models for flutter database generation." Computers & Fluids 197 (January 2020): 104372. http://dx.doi.org/10.1016/j.compfluid.2019.104372.

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Bonomo, Anthony L. "Multi-fidelity surrogate modeling for structural acoustics applications." Journal of the Acoustical Society of America 153, no. 3_supplement (2023): A287. http://dx.doi.org/10.1121/10.0018869.

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Recently, surrogate modeling methods have been explored for structural acoustics applications. These often involve evaluation of an “expensive” high-fidelity computational model to obtain training data. However, in many applications, models of varying fidelity and computational cost are available. In such situations, one can leverage multi-fidelity surrogate modeling, where the training data from models of varying fidelity are combined and simultaneously used to produce a surrogate model. A particularly popular class of multi-fidelity surrogate modeling techniques is known as co-Kriging, where

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7

Peart, Tanya, Nicolas Aubin, Stefano Nava, John Cater, and Stuart Norris. "Selection of Existing Sail Designs for Multi-Fidelity Surrogate Models." Journal of Sailing Technology 7, no. 01 (2022): 31–51. http://dx.doi.org/10.5957/jst/2022.7.2.31.

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Velocity Prediction Programs (VPPs) are commonly used to help predict and compare the performance of different sail designs. A VPP requires an aerodynamic input force matrix which can be computationally expensive to calculate, limiting its application in industrial sail design projects. The use of multi-fidelity kriging surrogate models has previously been presented by the authors to reduce this cost, with high-fidelity data for a new sail being modelled and the low-fidelity data provided by data from existing, but different, sail designs. The difference in fidelity is not due to the simulatio

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Peart, Tanya, Nicolas Aubin, Stefano Nava, John Cater, and Stuart Norris. "Multi-Fidelity Surrogate Models for VPP Aerodynamic Input Data." Journal of Sailing Technology 6, no. 01 (2021): 21–43. http://dx.doi.org/10.5957/jst/2021.6.1.21.

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Predicting the performance of a sail design is important for optimising the performance of a yacht, and Velocity Prediction Programs (VPPs) are commonly used for this purpose. The aerodynamic force data for a VPP is often calculated using Computational Fluid Dynamics (CFD) models, but these can be computationally expensive. A full VPP analysis for sail design is therefore usually restricted to high-budget design projects or research activities and is not practical for many industry projects. This work presents a method to reduce the computational cost of creating lift and drag force coefficien

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Farcaș, Ionuț-Gabriel, Benjamin Peherstorfer, Tobias Neckel, Frank Jenko, and Hans-Joachim Bungartz. "Context-aware learning of hierarchies of low-fidelity models for multi-fidelity uncertainty quantification." Computer Methods in Applied Mechanics and Engineering 406 (March 2023): 115908. http://dx.doi.org/10.1016/j.cma.2023.115908.

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Styler, Breelyn, and Reid Simmons. "Plan-Time Multi-Model Switching for Motion Planning." Proceedings of the International Conference on Automated Planning and Scheduling 27 (June 5, 2017): 558–66. http://dx.doi.org/10.1609/icaps.v27i1.13858.

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Robot navigation through non-uniform environments requires reliable motion plan generation. The choice of planning model fidelity can significantly impact performance. Prior research has shown that reducing model fidelity saves planning time, but sacrifices execution reliability. While current adaptive hierarchical motion planning techniques are promising, we present a framework that leverages a richer set of robot motion models at plan-time. The framework chooses when to switch models and what model is most applicable within a single trajectory. For instance, more complex environment locales

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11

Yi, Jin, Yichi Shen, and Christine A. Shoemaker. "A multi-fidelity RBF surrogate-based optimization framework for computationally expensive multi-modal problems with application to capacity planning of manufacturing systems." Structural and Multidisciplinary Optimization 62, no. 4 (2020): 1787–807. http://dx.doi.org/10.1007/s00158-020-02575-7.

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Abstract This paper presents a multi-fidelity RBF (radial basis function) surrogate-based optimization framework (MRSO) for computationally expensive multi-modal optimization problems when multi-fidelity (high-fidelity (HF) and low-fidelity (LF)) models are available. The HF model is expensive and accurate while the LF model is cheaper to compute but less accurate. To exploit the correlation between the LF and HF models and improve algorithm efficiency, in MRSO, we first apply the DYCORS (dynamic coordinate search algorithm using response surface) algorithm to search on the LF model and then e

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12

Sun, Qi, Tinghuan Chen, Siting Liu, Jianli Chen, Hao Yu, and Bei Yu. "Correlated Multi-objective Multi-fidelity Optimization for HLS Directives Design." ACM Transactions on Design Automation of Electronic Systems 27, no. 4 (2022): 1–27. http://dx.doi.org/10.1145/3503540.

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High-level synthesis (HLS) tools have gained great attention in recent years because it emancipates engineers from the complicated and heavy hardware description language writing and facilitates the implementations of modern applications (e.g., deep learning models) on Field-programmable Gate Array (FPGA) , by using high-level languages and HLS directives. However, finding good HLS directives is challenging, due to the time-consuming design processes, the balances among different design objectives, and the diverse fidelities (accuracies of data) of the performance values between the consecutiv

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13

Yoo, Kwangkyu, Omar Bacarreza, and M. H. Ferri Aliabadi. "Multi-fidelity robust design optimisation for composite structures based on low-fidelity models using successive high-fidelity corrections." Composite Structures 259 (March 2021): 113477. http://dx.doi.org/10.1016/j.compstruct.2020.113477.

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Song, Xueguan, Liye Lv, Wei Sun, and Jie Zhang. "A radial basis function-based multi-fidelity surrogate model: exploring correlation between high-fidelity and low-fidelity models." Structural and Multidisciplinary Optimization 60, no. 3 (2019): 965–81. http://dx.doi.org/10.1007/s00158-019-02248-0.

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Liu, Bo, Slawomir Koziel, and Nazar Ali. "SADEA-II: A generalized method for efficient global optimization of antenna design." Journal of Computational Design and Engineering 4, no. 2 (2016): 86–97. http://dx.doi.org/10.1016/j.jcde.2016.11.002.

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Abstract Efficiency improvement is of great significance for simulation-driven antenna design optimization methods based on evolutionary algorithms (EAs). The two main efficiency enhancement methods exploit data-driven surrogate models and/or multi-fidelity simulation models to assist EAs. However, optimization methods based on the latter either need ad hoc low-fidelity model setup or have difficulties in handling problems with more than a few design variables, which is a main barrier for industrial applications. To address this issue, a generalized three stage multi-fidelity-simulation-model

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16

Galindo, José, Roberto Navarro, Francisco Moya, and Andrea Conchado. "Comprehensive Method for Obtaining Multi-Fidelity Surrogate Models for Design Space Approximation: Application to Multi-Dimensional Simulations of Condensation Due to Mixing Streams." Applied Sciences 13, no. 11 (2023): 6361. http://dx.doi.org/10.3390/app13116361.

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In engineering problems, design space approximation using accurate computational models may require conducting a simulation for each explored working point, which is often not feasible in computational terms. For problems with numerous parameters and computationally demanding simulations, the possibility of resorting to multi-fidelity surrogates arises as a means to alleviate the effort by employing a reduced number of high-fidelity and expensive simulations and predicting a much cheaper low-fidelity model. A multi-fidelity approach for design space approximation is therefore proposed, requiri

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17

Younis, Adel, and Zuomin Dong. "High-Fidelity Surrogate Based Multi-Objective Optimization Algorithm." Algorithms 15, no. 8 (2022): 279. http://dx.doi.org/10.3390/a15080279.

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The employment of conventional optimization procedures that must be repeatedly invoked during the optimization process in real-world engineering applications is hindered despite significant gains in computing power by computationally expensive models. As a result, surrogate models that require far less time and resources to analyze are used in place of these time-consuming analyses. In multi-objective optimization (MOO) problems involving pricey analysis and simulation techniques such as multi-physics modeling and simulation, finite element analysis (FEA), and computational fluid dynamics (CFD

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18

Baldan, Marco, Alexander Nikanorov, and Bernard Nacke. "A parallel multi-fidelity optimization approach in induction hardening." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 39, no. 1 (2019): 133–43. http://dx.doi.org/10.1108/compel-05-2019-0221.

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Purpose Reliable modeling of induction hardening requires a multi-physical approach, which makes it time-consuming. In designing an induction hardening system, combining such model with an optimization technique allows managing a high number of design variables. However, this could lead to a tremendous overall computational cost. This paper aims to reduce the computational time of an optimal design problem by making use of multi-fidelity modeling and parallel computing. Design/methodology/approach In the multi-fidelity framework, the “high-fidelity” model couples the electromagnetic, thermal a

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19

Gurbuz, Caglar, Martin Eser, Johannes Schaffner, and Steffen Marburg. "A multi-fidelity Gaussian process for efficient frequency sweeps in the acoustic design of a vehicle cabin." Journal of the Acoustical Society of America 153, no. 4 (2023): 2006–18. http://dx.doi.org/10.1121/10.0017725.

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Highly accurate predictions from large-scale numerical simulations are associated with increased computational resources and time expense. Consequently, the data generation process can only be performed for a small sample size, limiting a detailed investigation of the underlying system. The concept of multi-fidelity modeling allows the combination of data from different models of varying costs and complexities. This study introduces a multi-fidelity model for the acoustic design of a vehicle cabin. Therefore, two models with different fidelity levels are used to solve the Helmholtz equation at

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20

Leguizamo, David Felipe, Hsin-Jung Yang, Xian Yeow Lee, and Soumik Sarkar. "Deep Reinforcement Learning for Robotic Control with Multi-Fidelity Models." IFAC-PapersOnLine 55, no. 37 (2022): 193–98. http://dx.doi.org/10.1016/j.ifacol.2022.11.183.

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Perron, Christian, Dushhyanth Rajaram, and Dimitri N. Mavris. "Multi-fidelity non-intrusive reduced-order modelling based on manifold alignment." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 477, no. 2253 (2021): 20210495. http://dx.doi.org/10.1098/rspa.2021.0495.

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This work presents the development of a multi-fidelity, parametric and non-intrusive reduced-order modelling method to tackle the problem of achieving an acceptable predictive accuracy under a limited computational budget, i.e. with expensive simulations and sparse training data. Traditional multi-fidelity surrogate models that predict scalar quantities address this issue by leveraging auxiliary data generated by a computationally cheaper lower fidelity code. However, for the prediction of field quantities, simulations of different fidelities may produce responses with inconsistent representat

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22

Li, Yang, Yu Shen, Jiawei Jiang, Jinyang Gao, Ce Zhang, and Bin Cui. "MFES-HB: Efficient Hyperband with Multi-Fidelity Quality Measurements." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 10 (2021): 8491–500. http://dx.doi.org/10.1609/aaai.v35i10.17031.

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Hyperparameter optimization (HPO) is a fundamental problem in automatic machine learning (AutoML). However, due to the expensive evaluation cost of models (e.g., training deep learning models or training models on large datasets), vanilla Bayesian optimization (BO) is typically computationally infeasible. To alleviate this issue, Hyperband (HB) utilizes the early stopping mechanism to speed up configuration evaluations by terminating those badly-performing configurations in advance. This leads to two kinds of quality measurements: (1) many low-fidelity measurements for configurations that get

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Bonfiglio, Luca, Paris Perdikaris, and Stefano Brizzolara. "Multi-fidelity Bayesian Optimization of SWATH Hull Forms." Journal of Ship Research 64, no. 02 (2020): 154–70. http://dx.doi.org/10.5957/jsr.2020.64.2.154.

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This study presents a multi-fidelity framework that enables the construction of surrogate models capable of capturing complex correlations between design variables and quantities of interest. Resistance in calm water is investigated for a SWATH hull in a multidimensional design space using a new method to derive high-quality response surfaces through machine learning techniques based on a low number of high-fidelity computations and a larger number of less-expensive low-fidelity computations. First, a verification and validation study is presented with the goal of comparing and ranking numeric

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Leifsson, Leifur, and Slawomir Koziel. "Adaptive response prediction for aerodynamic shape optimization." Engineering Computations 34, no. 5 (2017): 1485–500. http://dx.doi.org/10.1108/ec-02-2016-0070.

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Purpose The purpose of this paper is to reduce the overall computational time of aerodynamic shape optimization that involves accurate high-fidelity simulation models. Design/methodology/approach The proposed approach is based on the surrogate-based optimization paradigm. In particular, multi-fidelity surrogate models are used in the optimization process in place of the computationally expensive high-fidelity model. The multi-fidelity surrogate is constructed using physics-based low-fidelity models and a proper correction. This work introduces a novel correction methodology – referred to as th

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Konrad, Julia, Ionuţ-Gabriel Farcaş, Benjamin Peherstorfer, et al. "Data-driven low-fidelity models for multi-fidelity Monte Carlo sampling in plasma micro-turbulence analysis." Journal of Computational Physics 451 (February 2022): 110898. http://dx.doi.org/10.1016/j.jcp.2021.110898.

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Baldo, Leonardo, Pier Carlo Berri, Matteo D. L. Dalla Vedova, and Paolo Maggiore. "Experimental Validation of Multi-fidelity Models for Prognostics of Electromechanical Actuators." PHM Society European Conference 7, no. 1 (2022): 32–42. http://dx.doi.org/10.36001/phme.2022.v7i1.3347.

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The growing adoption of electrical energy as a secondary form of onboard power leads to an increase of electromechanical actuators (EMAs) use in aerospace applications. Therefore, innovative prognostic and diagnostic methodologies are becoming a fundamental tool to early identify faults propagation, prevent performance degradation, and ensure an acceptable level of safety and reliability of the system. Furthermore, prognostics entails further advantages, including a better ability to plan the maintenance of the various equipment, manage the warehouse and maintenance personnel, and a reduction

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Morse, Llewellyn, Zahra Sharif Khodaei, and M. H. Aliabadi. "Multi-Fidelity Modeling-Based Structural Reliability Analysis with the Boundary Element Method." Journal of Multiscale Modelling 08, no. 03n04 (2017): 1740001. http://dx.doi.org/10.1142/s1756973717400017.

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In this work, a method for the application of multi-fidelity modeling to the reliability analysis of 2D elastostatic structures using the boundary element method (BEM) is proposed. Reliability analyses were carried out on a rectangular plate with a center circular hole subjected to uniaxial tension using Monte Carlo simulations (MCS), the first-order reliability method (FORM), and the second-order reliability method (SORM). Two BEM models were investigated, a low-fidelity model (LFM) of 20 elements and a high-fidelity model (HFM) of 100 elements. The response of these models at several design

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Amrit, Anand, and Leifur Leifsson. "Applications of surrogate-assisted and multi-fidelity multi-objective optimization algorithms to simulation-based aerodynamic design." Engineering Computations 37, no. 2 (2019): 430–57. http://dx.doi.org/10.1108/ec-12-2018-0553.

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Purpose The purpose of this work is to apply and compare surrogate-assisted and multi-fidelity, multi-objective optimization (MOO) algorithms to simulation-based aerodynamic design exploration. Design/methodology/approach The three algorithms for multi-objective aerodynamic optimization compared in this work are the combination of evolutionary algorithms, design space reduction and surrogate models, the multi-fidelity point-by-point Pareto set identification and the multi-fidelity sequential domain patching (SDP) Pareto set identification. The algorithms are applied to three cases, namely, an

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Lin, James T., Chun-Chih Chiu, Edward Huang, and Hung-Ming Chen. "A Multi-Fidelity Model Approach for Simultaneous Scheduling of Machines and Vehicles in Flexible Manufacturing Systems." Asia-Pacific Journal of Operational Research 35, no. 01 (2018): 1850005. http://dx.doi.org/10.1142/s0217595918500057.

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Driven by sensor technologies and Internet of Things, massive real-time data from highly interconnected devices are available, which enables the improvement of decision-making quality. Scheduling of such production systems can be challenging as it must incorporate the latest data and be able to re-plan quickly. In this research, a multi-fidelity model for simultaneous scheduling problem of machines and vehicles at flexible manufacturing system has been proposed. In order to improve the computational efficiency, we extend the framework, called multi-fidelity optimization with ordinal transforma

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Fu, Wenbo, Qiushi Li, Yongshun Song, Yaogen Shu, Zhongcan Ouyang, and Ming Li. "Theoretical analysis of RNA polymerase fidelity: a steady-state copolymerization approach." Communications in Theoretical Physics 74, no. 1 (2021): 015601. http://dx.doi.org/10.1088/1572-9494/ac3993.

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Abstract The fidelity of DNA transcription catalyzed by RNA polymerase (RNAP) has long been an important issue in biology. Experiments have revealed that RNAP can incorporate matched nucleotides selectively and proofread the incorporated mismatched nucleotides. However, systematic theoretical researches on RNAP fidelity are still lacking. In the last decade, several theories on RNA transcription have been proposed, but they only handled highly simplified models without considering the high-order neighbor effects and the oligonucleotides cleavage both of which are critical for the overall fidel

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Koziel, Slawomir, Yonatan Tesfahunegn, and Leifur Leifsson. "Variable-fidelity CFD models and co-Kriging for expedited multi-objective aerodynamic design optimization." Engineering Computations 33, no. 8 (2016): 2320–38. http://dx.doi.org/10.1108/ec-09-2015-0277.

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Purpose Strategies for accelerated multi-objective optimization of aerodynamic surfaces are investigated, including the possibility of exploiting surrogate modeling techniques for computational fluid dynamic (CFD)-driven design speedup of such surfaces. The purpose of this paper is to reduce the overall optimization time. Design/methodology/approach An algorithmic framework is described that is composed of: a search space reduction, fast surrogate models constructed using variable-fidelity CFD models and co-Kriging, and Pareto front refinement. Numerical case studies are provided demonstrating

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Ellison, M., F. A. DiazDelaO, N. Z. Ince, and M. Willetts. "Robust optimisation of computationally expensive models using adaptive multi-fidelity emulation." Applied Mathematical Modelling 100 (December 2021): 92–106. http://dx.doi.org/10.1016/j.apm.2021.07.020.

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SONG, Chao, Xudong YANG, and Wenping SONG. "Multi-infill strategy for kriging models used in variable fidelity optimization." Chinese Journal of Aeronautics 31, no. 3 (2018): 448–56. http://dx.doi.org/10.1016/j.cja.2018.01.011.

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Pilania, G., J. E. Gubernatis, and T. Lookman. "Multi-fidelity machine learning models for accurate bandgap predictions of solids." Computational Materials Science 129 (March 2017): 156–63. http://dx.doi.org/10.1016/j.commatsci.2016.12.004.

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Du, Wenting, and Jin Su. "Uncertainty Quantification for Numerical Solutions of the Nonlinear Partial Differential Equations by Using the Multi-Fidelity Monte Carlo Method." Applied Sciences 12, no. 14 (2022): 7045. http://dx.doi.org/10.3390/app12147045.

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The Monte Carlo simulation is a popular statistical method to estimate the effect of uncertainties on the solutions of nonlinear partial differential equations, but it requires a huge computational cost of the deterministic model, and the convergence may become slow. For this reason, we developed the multi-fidelity Monte Carlo (MFMC) methods based on data-driven low-fidelity models for uncertainty analysis of nonlinear partial differential equations. Firstly, the nonlinear partial differential equations are transformed into ordinary differential equations (ODEs) by using finite difference disc

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Xu, Jie, Si Zhang, Edward Huang, Chun-Hung Chen, Loo Hay Lee, and Nurcin Celik. "MO2TOS: Multi-Fidelity Optimization with Ordinal Transformation and Optimal Sampling." Asia-Pacific Journal of Operational Research 33, no. 03 (2016): 1650017. http://dx.doi.org/10.1142/s0217595916500172.

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Simulation optimization can be used to solve many complex optimization problems in automation applications such as job scheduling and inventory control. We propose a new framework to perform efficient simulation optimization when simulation models with different fidelity levels are available. The framework consists of two novel methodologies: ordinal transformation (OT) and optimal sampling (OS). The OT methodology uses the low-fidelity simulations to transform the original solution space into an ordinal space that encapsulates useful information from the low-fidelity model. The OS methodology

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Avramova, Maria, Agustin Abarca, Jason Hou, and Kostadin Ivanov. "Innovations in Multi-Physics Methods Development, Validation, and Uncertainty Quantification." Journal of Nuclear Engineering 2, no. 1 (2021): 44–56. http://dx.doi.org/10.3390/jne2010005.

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This paper provides a review of current and upcoming innovations in development, validation, and uncertainty quantification of nuclear reactor multi-physics simulation methods. Multi-physics modelling and simulations (M&S) provide more accurate and realistic predictions of the nuclear reactors behavior including local safety parameters. Multi-physics M&S tools can be subdivided in two groups: traditional multi-physics M&S on assembly/channel spatial scale (currently used in industry and regulation), and novel high-fidelity multi-physics M&S on pin (sub-pin)/sub-channel spatial

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Klimczyk, Witold Artur, and Zdobyslaw Jan Goraj. "Analysis and optimization of morphing wing aerodynamics." Aircraft Engineering and Aerospace Technology 91, no. 3 (2019): 538–46. http://dx.doi.org/10.1108/aeat-12-2017-0289.

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PurposeThe purpose of this paper is to present a method for analysis and optimization of morphing wing. Moreover, a numerical advantage of morphing airfoil wing, typically assessed in simplified two-dimensional analysis is found using higher fidelity methods.Design/methodology/approachBecause of multi-point nature of morphing wing optimization, an approach for optimization by analysis is presented. Starting from naïve parametrization, multi-fidelity aerodynamic data are used to construct response surface model. From the model, many significant information are extracted related to parameters ef

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Deng, Xinjian, Enying Li, and Hu Wang. "A Variable-Fidelity Multi-Objective Evolutionary Method for Polygonal Pin Fin Heat Sink Design." Sustainability 15, no. 2 (2023): 1104. http://dx.doi.org/10.3390/su15021104.

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For the multi-objective design of heat sinks, several evolutionary algorithms usually require many iterations to converge, which is computationally expensive. Variable-fidelity multi-objective (VFO) methods were suggested to improve the efficiency of evolutionary algorithms. However, multi-objective problems are seldom optimized using VFO. Therefore, a variable-fidelity evolutionary method (VFMEM) was suggested. Similar to other variable-fidelity algorithms, VFMEM solves a high-fidelity model using a low-fidelity model. Compared with other algorithms, the distinctive characteristic of VFMEM is

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He, Lei, Weiqi Qian, Tun Zhao, and Qing Wang. "Multi-Fidelity Aerodynamic Data Fusion with a Deep Neural Network Modeling Method." Entropy 22, no. 9 (2020): 1022. http://dx.doi.org/10.3390/e22091022.

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To generate more high-quality aerodynamic data using the information provided by different fidelity data, where low-fidelity aerodynamic data provides the trend information and high-fidelity aerodynamic data provides value information, we applied a deep neural network (DNN) algorithm to fuse the information of multi-fidelity aerodynamic data. We discuss the relationships between the low-fidelity and high-fidelity data, and then we describe the proposed architecture for an aerodynamic data fusion model. The architecture consists of three fully-connected neural networks that are employed to appr

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Yang, Chih-Hsuan, Balaji Sesha Sarath Pokuri, Xian Yeow Lee, et al. "Multi-fidelity machine learning models for structure–property mapping of organic electronics." Computational Materials Science 213 (October 2022): 111599. http://dx.doi.org/10.1016/j.commatsci.2022.111599.

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Zhang, Chi, Chaolin Song, and Abdollah Shafieezadeh. "Adaptive reliability analysis for multi-fidelity models using a collective learning strategy." Structural Safety 94 (January 2022): 102141. http://dx.doi.org/10.1016/j.strusafe.2021.102141.

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Koziel, Slawomir, and Stanislav Ogurtsov. "Multi-Objective Design of Antennas Using Variable-Fidelity Simulations and Surrogate Models." IEEE Transactions on Antennas and Propagation 61, no. 12 (2013): 5931–39. http://dx.doi.org/10.1109/tap.2013.2283599.

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Thandayutham, Karthikeyan, and Abdus Samad. "Hydrostructural Optimization of a Marine Current Turbine Through Multi-fidelity Numerical Models." Arabian Journal for Science and Engineering 45, no. 2 (2019): 935–52. http://dx.doi.org/10.1007/s13369-019-04185-y.

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Yang, Yibo, and Paris Perdikaris. "Conditional deep surrogate models for stochastic, high-dimensional, and multi-fidelity systems." Computational Mechanics 64, no. 2 (2019): 417–34. http://dx.doi.org/10.1007/s00466-019-01718-y.

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Eaton, Ammon N., Logan D. R. Beal, Samuel D. Thorpe, et al. "Real time model identification using multi-fidelity models in managed pressure drilling." Computers & Chemical Engineering 97 (February 2017): 76–84. http://dx.doi.org/10.1016/j.compchemeng.2016.11.008.

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Guo, Qi, Jiutao Hang, Suian Wang, Wenzhi Hui, and Zonghong Xie. "Design optimization of variable stiffness composites by using multi-fidelity surrogate models." Structural and Multidisciplinary Optimization 63, no. 1 (2020): 439–61. http://dx.doi.org/10.1007/s00158-020-02684-3.

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Babaee, H., P. Perdikaris, C. Chryssostomidis, and G. E. Karniadakis. "Multi-fidelity modelling of mixed convection based on experimental correlations and numerical simulations." Journal of Fluid Mechanics 809 (November 21, 2016): 895–917. http://dx.doi.org/10.1017/jfm.2016.718.

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For thermal mixed-convection flows, the Nusselt number is a function of Reynolds number, Grashof number and the angle between the forced- and natural-convection directions. We consider flow over a heated cylinder for which there is no universal correlation that accurately predicts Nusselt number as a function of these parameters, especially in opposing-convection flows, where the natural convection is against the forced convection. Here, we revisit this classical problem by employing modern tools from machine learning to develop a general multi-fidelity framework for constructing a stochastic

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Quattrocchi, Gaetano, Matteo D. L. Dalla Vedova, and Pier Carlo Berri. "Lumped parameters multi-fidelity digital twins for prognostics of electromechanical actuators." Journal of Physics: Conference Series 2526, no. 1 (2023): 012076. http://dx.doi.org/10.1088/1742-6596/2526/1/012076.

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Abstract The growing affirmation of on-board systems based on all-electric secondary power sources is causing a progressive diffusion of electromechanical actuators (EMA) in aerospace applications. As a result, novel prognostic and diagnostic approaches are becoming a critical tool for detecting fault propagation early, preventing EMA performance deterioration, and ensuring acceptable levels of safety and reliability of the system. These approaches often require the development of various types of multiple numerical models capable of simulating the performance of the EMA with different levels

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Liu, H., M. Hou, A. Li, and L. Xie. "AN AUTOMATIC EXTRACTION METHOD FOR THE PARAMETERS OF MULTI-LOD BIM MODELS FOR TYPICAL COMPONENTS OF WOODEN ARCHITECTURAL HERITAGE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W15 (August 23, 2019): 679–85. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w15-679-2019.

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<p><strong>Abstract.</strong> A demand-oriented Building Information Model (BIM) model built using high-fidelity point cloud data can better protect architectural heritage. The multi-level detail (mutli-LoD) parametric model emphasizes the different protection requirements of typical components and the automatic extraction of corresponding parameters of high-fidelity point clouds, which are two related key issues. Taking the typical Chinese wooden architectural heritage as an example, according to different requirements, the multi-LoD principle of typical components is propos

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