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Journal articles on the topic 'Multi-fidelity models'

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Author: Grafiati
Published: 29 August 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 (February 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 wrong trends and erroneous predictions if probed outside of their validity regime. Here we put forth a probabilistic framework based on Gaussian process regression and nonlinear autoregressive schemes that is capable of learning complex nonlinear and space-dependent cross-correlations between models of variable fidelity, and can effectively safeguard against low-fidelity models that provide wrong trends. This introduces a new class of multi-fidelity information fusion algorithms that provide a fundamental extension to the existing linear autoregressive methodologies, while still maintaining the same algorithmic complexity and overall computational cost. The performance of the proposed methods is tested in several benchmark problems involving both synthetic and real multi-fidelity datasets from computational fluid dynamics simulations.
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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 (March 21, 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 include non-polynomial and polynomial functions of various input dimensions, lower dimensional heterogeneous non-polynomial functions, as well as a coupled spring-mass-system. Overall, multi-fidelity models are more accurate than high-fidelity ones for the same cost, especially when only a few high-fidelity training points are employed. Full-order PCEs tend to be a factor of two or so worse than SPCES in terms of overall accuracy. The combination of the two approaches into the SPCE-Kriging model leads to a more accurate and flexible method overall.
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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 (March 1, 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 simulation output from both “expensive” and “cheap” computational models are correlated and a correction process is obtained that maps between the results of these models of varying fidelity. This talk will review co-Kriging and demonstrate its utility on a canonical structural acoustics problem. [Work supported by the Office of Naval Research.]
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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 (January 5, 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 simulation method used to obtain the data, but instead how similar the sail’s geometry is to the new sail design. An important consideration for the construction of these models is the choice of low-fidelity data points, which provide information about the trend of the model curve between the high-fidelity data. A method is required to select the best existing sail design to use for the low-fidelity data when constructing a multi-fidelity model. The suitability of an existing sail design as a low fidelity model could be evaluated based on the similarity of its geometric parameters with the new sail. It is shown here that for upwind jib sails, the similarity of the broadseam between the two sails best indicates the ability of a design to be used as low-fidelity data for a lift coefficient surrogate model. The lift coefficient surrogate model error predicted by the regression is shown to be close to 1% of the lift coefficient surrogate error for most points. Larger discrepancies are observed for a drag coefficient surrogate error regression.
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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 (February 9, 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 coefficient curves for input into a VPP using both multi-fidelity kriging surrogate modelling and data from existing sail designs. This method is shown to reduce the number of CFD simulations required for a desired accuracy when compared to a single-fidelity model. A maximum reduction in the required computational effort of 57% was achieved for model-scale symmetric spinnaker sails. For the same number of simulations, the accuracy of the model predictions was improved by up to 72% for scale-symmetric spinnaker sails, and 90% for asymmetric spinnakers.
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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 require higher fidelity models, while lower fidelity models are sufficient for simpler parts of the planning space, thus saving plan time. Our algorithm continuously aims to pick the model that best handles the current local environment. This effectively generates a single, mixed-fidelity plan. We present results for a simulated mobile robot with attached trailer in a hospital domain. We compare using a single motion planning model to switching with our framework of multiple models. Our results demonstrate that multi-fidelity model switching increases plan-time efficiency without sacrificing execution reliability.
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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 (May 17, 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 employ a potential area detection procedure to identify the promising points from the LF model. The promising points serve as the initial start points when we further search for the optimal solution based on the HF model. The performance of MRSO is compared with 6 other surrogate-based optimization methods (4 are using a single-fidelity surrogate and the rest 2 are using multi-fidelity surrogates). The comparisons are conducted on a multi-fidelity optimization test suite containing 10 problems with 10 and 30 dimensions. Besides the benchmark functions, we also apply the proposed algorithm to a practical and computationally expensive capacity planning problem in manufacturing systems which involves discrete event simulations. The experimental results demonstrate that MRSO outperforms all the compared methods.
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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 (July 31, 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 consecutive FPGA design stages. To find good HLS directives, a novel automatic optimization algorithm is proposed to explore the Pareto designs of the multiple objectives while making full use of the data with different fidelities from different FPGA design stages. Firstly, a non-linear Gaussian process (GP) is proposed to model the relationships among the different FPGA design stages. Secondly, for the first time, the GP model is enhanced as correlated GP (CGP) by considering the correlations between the multiple design objectives, to find better Pareto designs. Furthermore, we extend our model to be a deep version deep CGP (DCGP) by using the deep neural network to improve the kernel functions in Gaussian process models, to improve the characterization capability of the models, and learn better feature representations. We test our design method on some public benchmarks (including general matrix multiplication and sparse matrix-vector multiplication) and deep learning-based object detection model iSmart2 on FPGA. Experimental results show that our methods outperform the baselines significantly and facilitate the deep learning designs on FPGA.
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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 (April 1, 2019): 965–81. http://dx.doi.org/10.1007/s00158-019-02248-0.

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15

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 (November 20, 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 assisted antenna design optimization framework is proposed in this paper. The main ideas include introduction of a novel data mining stage handling the discrepancy between simulation models of different fidelities, and a surrogate-model-assisted combined global and local search stage for efficient high-fidelity simulation model-based optimization. This framework is then applied to SADEA, which is a state-of-the-art surrogate-model-assisted antenna design optimization method, constructing SADEA-II. Experimental results indicate that SADEA-II successfully handles various discrepancy between simulation models and considerably outperforms SADEA in terms of computational efficiency while ensuring improved design quality. Highlights An EFFICIENT antenna design global optimization method for problems requiring very expensive EM simulations. A new multi-fidelity surrogate-model-based optimization framework to perform RELIABLE efficient global optimization A data mining method to address distortions of EM models of different fidelities (bottleneck of multi-fidelity design).
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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 (May 23, 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, requiring two different designs of experiments to assess the best combination of surrogate models and an intermediate meta-modeled variable. The strategy is applied to the prediction of condensation that occurs when two humid air streams are mixed in a three-way junction, which occurs when using low-pressure exhaust gas recirculation to reduce piston engine emissions. In this particular case, most of the assessed combinations of surrogate and intermediate variables provide a good agreement between observed and predicted values, resulting in the lowest normalized mean absolute error (3.4%) by constructing a polynomial response surface using a multi-fidelity additive scaling variable that calculates the difference between the low-fidelity and high-fidelity predictions of the condensation mass flow rate.
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Younis, Adel, and Zuomin Dong. "High-Fidelity Surrogate Based Multi-Objective Optimization Algorithm." Algorithms 15, no. 8 (August 7, 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), surrogate models are found to be a promising endeavor, particularly for the optimization of complex engineering design problems involving black box functions. In order to reduce the expense of fitness function evaluations and locate the Pareto frontier for MOO problems, the automated multiobjective surrogate based Pareto finder MOO algorithm (AMSP) is proposed. Utilizing data samples taken from the feasible design region, the algorithm creates three surrogate models. The algorithm repeats the process of sampling and updating the Pareto set, by assigning weighting factors to those surrogates in accordance with the values of the root mean squared error, until a Pareto frontier is discovered. AMSP was successfully employed to identify the Pareto set and the Pareto border. Utilizing multi-objective benchmark test functions and engineering design examples such airfoil shape geometry of wind turbine, the unique approach was put to the test. The cost of computing the Pareto optima for test functions and real engineering design problem is reduced, and promising results were obtained.
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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 (November 27, 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 and metallurgical fields. It predicts the phase transformations during both the heating and cooling stages. The “low-fidelity” model is instead limited to the heating step. Its inaccuracy is counterbalanced by its cheapness, which makes it suitable for exploring the design space in optimization. Then, the use of co-Kriging allows merging information from different fidelity models and predicting good design candidates. Field evaluations of both models occur in parallel. Findings In the design of an induction heating system, the synergy between the “high-fidelity” and “low-fidelity” model, together with use of surrogates and parallel computing could reduce up to one order of magnitude the overall computational cost. Practical implications On one hand, multi-physical modeling of induction hardening implies a better understanding of the process, resulting in further potential process improvements. On the other hand, the optimization technique could be applied to many other computationally intensive real-life problems. Originality/value This paper highlights how parallel multi-fidelity optimization could be used in designing an induction hardening system.
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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 (April 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 specified frequencies with the boundary element method. Gaussian processes (GPs) are trained on each fidelity level with the simulation results to predict the unknown system response. In this way, the multi-fidelity model enables an efficient approximation of the frequency sweep for acoustics in the frequency domain. Additionally, the proposed method inherently considers uncertainties due to the data generation process. To demonstrate the effectiveness of our framework, the multifrequency solution is validated with the high-fidelity (HF) solution at each frequency. The results show that the frequency sweep is efficiently approximated by using only a limited number of HF simulations. Thus, these findings indicate that multi-fidelity GPs can be adopted for fast and, simultaneously, accurate predictions.
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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 (September 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 representations, rendering the direct application of common multi-fidelity techniques challenging. The proposed approach uses manifold alignment to fuse inconsistent fields from high- and low-fidelity simulations by individually projecting their solution onto a common latent space. Hence, simulations using incompatible grids or geometries can be combined into a single multi-fidelity reduced-order model without additional manipulation of the data. This method is applied to a variety of multi-fidelity scenarios using a transonic airfoil problem. In most cases, the new multi-fidelity reduced-order model achieves comparable predictive accuracy at a lower computational cost. Furthermore, it is demonstrated that the proposed method can combine disparate fields without any adverse effect on predictive performance.
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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 (May 18, 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 early-stopped, and (2) few high-fidelity measurements for configurations that are evaluated without being early stopped. The state-of-the-art HB-style method, BOHB, aims to combine the benefits of both BO and HB. Instead of sampling configurations randomly in HB, BOHB samples configurations based on a BO surrogate model, which is constructed with the high-fidelity measurements only. However, the scarcity of high-fidelity measurements greatly hampers the efficiency of BO to guide the configuration search. In this paper, we present MFES-HB, an efficient Hyperband method that is capable of utilizing both the high-fidelity and low-fidelity measurements to accelerate the convergence of HPO tasks. Designing MFES-HB is not trivial as the low-fidelity measurements can be biased yet informative to guide the configuration search. Thus we propose to build a Multi-Fidelity Ensemble Surrogate (MFES) based on the generalized Product of Experts framework, which can integrate useful information from multi-fidelity measurements effectively. The empirical studies on the real-world AutoML tasks demonstrate that MFES-HB can achieve 3.3-8.9x speedups over the state-of-the-art approach --- BOHB.
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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 (June 1, 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 numerical methods against experiments performed on a conventional SWATH geometry. Then, the hull geometry of a new family of unconventional SWATH hull forms with twin counter-canted struts is parametrically defined and sequentially refined using multi-fidelity Bayesian optimization. Ship resistance in calm water is finally predicted using observations from two different fidelity levels. We demonstrate that the multi-fidelity optimization framework is successful in obtaining an optimized design using a small number of high-fidelity computations and a larger number of low-fidelity computations. Simulation and optimization costs are reduced by orders of magnitude, providing accurate certificates of fidelity for the performance of the proposed design.
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Leifsson, Leifur, and Slawomir Koziel. "Adaptive response prediction for aerodynamic shape optimization." Engineering Computations 34, no. 5 (July 3, 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 the adaptive response prediction (ARP). The ARP technique corrects the low-fidelity model response, represented by the airfoil pressure distribution, through suitable horizontal and vertical adjustments. Findings Numerical investigations show the feasibility of solving real-world problems involving optimization of transonic airfoil shapes and accurate computational fluid dynamics simulation models of such surfaces. The results show that the proposed approach outperforms traditional surrogate-based approaches. Originality/value The proposed aerodynamic design optimization algorithm is novel and holistic. In particular, the ARP correction technique is original. The algorithm is useful for fast design of aerodynamic surfaces using high-fidelity simulation data in moderately sized search spaces, which is challenging using conventional methods because of excessive computational costs.
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Konrad, Julia, Ionuţ-Gabriel Farcaş, Benjamin Peherstorfer, Alessandro Di Siena, Frank Jenko, Tobias Neckel, and Hans-Joachim Bungartz. "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 (June 29, 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 in system management costs. Frequently, such approaches require the development of typologies of numerical models capable of simulating the performance of the EMA with different levels of fidelity: monitoring models, suitably simplified to combine speed and accuracy with reduced computational costs, and high fidelity models (and high computational intensity), to generate databases, develop predictive algorithms and train machine learning surrogates. Because of this, the authors developed a high-fidelity multi-domain numerical model (HF) capable of accounting for a variety of physical phenomena and gradual failures in the EMA, as well as a low-fidelity counterpart (LF). This simplified model is derived by the HF and intended for monitoring applications. While maintaining a low computing cost, LF is fault sensitive and can simulate the system position, speed, and equivalent phase currents. These models have been validated using a dedicated EMA test bench, designed and implemented by authors. The HF model can simulate the operation of the actuator in nominal conditions as well as in the presence of incipient mechanical faults, such as a variation in friction and an increase of backlash in the reduction gearbox. Comparing the preliminary results highlights satisfactory consistency between the experimental test bench and the two numerical models proposed by the authors.
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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 (September 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 points was used to create multi-fidelity models (MFMs) utilizing second-order polynomial response surfaces and their reliability, alongside that of the LFM and the HFM, was evaluated. Results show that the MFMs that directly called the LFM were significantly superior in terms of accuracy to the LFM, achieving very similar levels of accuracy to the HFM, while also being of similar computational cost to the LFM. These direct MFMs were found to provide good substitutes for the HFM for MCS, FORM, and SORM.
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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 (August 9, 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 analytical test case, the design of transonic airfoil shapes and the design of subsonic wing shapes, and are evaluated based on the resulting best possible trade-offs and the computational overhead. Findings The results show that all three algorithms yield comparable best possible trade-offs for all the test cases. For the aerodynamic test cases, the multi-fidelity Pareto set identification algorithms outperform the surrogate-assisted evolutionary algorithm by up to 50 per cent in terms of cost. Furthermore, the point-by-point algorithm is around 27 per cent more efficient than the SDP algorithm. Originality/value The novelty of this work includes the first applications of the SDP algorithm to multi-fidelity aerodynamic design exploration, the first comparison of these multi-fidelity MOO algorithms and new results of a complex simulation-based multi-objective aerodynamic design of subsonic wing shapes involving two conflicting criteria, several nonlinear constraints and over ten design variables.
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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 (February 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 transformation and optimal sampling, with combining with the K-means method. The proposed framework enables the benefits of both fast and inexpensive low-fidelity models with accurate but more expensive high-fidelity models. Results show that this approach can significantly decrease computational cost compared with other algorithms in the literature.
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30

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 (December 10, 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 fidelity. In this paper, we regard RNA transcription as a binary copolymerization process and calculate the transcription fidelity by the steady-state copolymerization theory recently proposed by us for DNA replication. With this theory, the more realistic models considering higher-order neighbor effects, oligonucleotides cleavage, multi-step incorporation and multi-step cleavage can be rigorously handled.
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31

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 (November 7, 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 the feasibility of solving real-world problems involving multi-objective optimization of transonic airfoil shapes and accurate CFD simulation models of such surfaces. Findings It is possible, through appropriate combination of surrogate modeling techniques and variable-fidelity models, to identify a set of alternative designs representing the best possible trade-offs between conflicting design objectives in a realistic time frame corresponding to a few dozen of high-fidelity CFD simulations of the respective surfaces. Originality/value The proposed aerodynamic design optimization algorithmic framework is novel and holistic. It proved useful for fast design of aerodynamic surfaces using high-fidelity simulation data in moderately sized search space, which is extremely challenging when using conventional methods due to the excessive computational cost.
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32

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 (March 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 (July 12, 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 discretization or Fourier transformation. Then, the reduced dimension model and discrete empirical interpolation method (DEIM) are coupled to construct effective nonlinear low-fidelity models in ODEs system. Finally, the MFMC method is used to combine the output information of the high-fidelity model and the low-fidelity models to give the optimal estimation of the statistics. Experimental results of the nonlinear Schrodinger equation and the Burgers’ equation show that, compared with the standard Monte Carlo method, the MFMC method based on the data-driven low-fidelity model in this paper can improve the calculation efficiency significantly.
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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 (June 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 efficiently uses high-fidelity simulations to sample the transformed space in search of the optimal solution. Through theoretical analysis and numerical experiments, we demonstrate the promising performance of the multi-fidelity optimization with ordinal transformation and optimal sampling (MO2TOS) framework.
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37

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 (March 7, 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 scale. The current trends in reactor design and safety analysis are towards further development, verification, and validation of multi-physics multi-scale M&S combined with uncertainty quantification and propagation. Approaches currently applied for validation of the traditional multi-physics M&S are summarized and illustrated using established Nuclear Energy Agency/Organization for Economic Cooperation and Development (NEA/OECD) multi-physics benchmarks. Novel high-fidelity multi-physics M&S allow for insights crucial to resolve industry challenge and high impact problems previously impossible with the traditional tools. Challenges in validation of novel multi-physics M&S are discussed along with the needs for developing validation benchmarks based on experimental data. Due to their complexity, the novel multi-physics codes are still computationally expensive for routine applications. This fact motivates the use of high-fidelity novel models and codes to inform the low-fidelity traditional models and codes, leading to improved traditional multi-physics M&S. The uncertainty quantification and propagation across different scales (multi-scale) and multi-physics phenomena are demonstrated using the OECD/NEA Light Water Reactor Uncertainty Analysis in Modelling benchmark framework. Finally, the increasing role of data science and analytics techniques in development and validation of multi-physics M&S is summarized.
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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 (March 4, 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 effect on objective; hence, design sensitivity and, ultimately, optimal solution can be found.FindingsThe method was tested on benchmark problem, with some easy-to-predict results. All of them were confirmed, along with additional information on morphing trailing edge wings. It was found that wing with morphing trailing edge has around 10 per cent lower drag for the same lift requirement when compared to conventional design.Practical implicationsIt is demonstrated that providing a smooth surface on wing gives substantial improvement in multi-purpose aircrafts. Details on how this is achieved are described. The metodology and results presented in current paper can be used in further development of morphing wing.Originality/valueMost of literature describing morphing airfoil design, optimization or calculations, performs only 2D analysis. Furthermore, the comparison is often based on low-fidelity aerodynamic models. This paper uses 3D, multi-fidelity aerodynamic models. The results confirm that this approach reveals information unavailable with simplified models.
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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 (January 6, 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 its application in multi-objective optimization. First, the suggested method uses a low-fidelity model to locate the region where the global optimal solution might be found. Sequentially, both high- and low-fidelity models can be integrated to find the real global optimal solution. Circulation distance elimination (CDE) was suggested to uniformly obtain the PF. To evaluate the feasibility of VFMEM, two classical benchmark functions were tested. Compared with the widely used multi-objective particle swarm optimization (MOPSO), the efficiency of VFMEM was significantly improved and the Pareto frontier (PFs) could also be obtained. To evaluate the algorithm’s feasibility, a polygonal pin fin heat sink (PFHS) design was carried out using VFMEM. Compared with the initial design, the results showed that the mass, base temperature, and temperature difference of the designed optimum heat sink were decreased 5.5%, 18.5%, and 62.0%, respectively. More importantly, if the design was completed directly by MOPSO, the computational cost of the entire optimization procedure would be significantly increased.
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40

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 (September 12, 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 approximate low-fidelity data, and the linear part and nonlinear part of correlation for the low- and high-fidelity data, respectively. To test the proposed multi-fidelity aerodynamic data fusion method, we calculated Euler and Navier–Stokes simulations for a typical airfoil at various Mach numbers and angles of attack to obtain the aerodynamic coefficients as low- and high-fidelity data. A fusion model of the longitudinal coefficients of lift CL and drag CD was constructed with the proposed method. For comparisons, variable complexity modeling and cokriging models were also built. The accuracy spread between the predicted value and true value was discussed for both the training and test data of the three different methods. We calculated the root mean square error and average relative deviation to demonstrate the performance of the three different methods. The fusion result of the proposed method was satisfactory on the test case, and showed a better performance compared with the other two traditional methods presented. The results provide evidence that the method proposed in this paper can be useful in dealing with the multi-fidelity aerodynamic data fusion problem.
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Yang, Chih-Hsuan, Balaji Sesha Sarath Pokuri, Xian Yeow Lee, Sangeeth Balakrishnan, Chinmay Hegde, Soumik Sarkar, and Baskar Ganapathysubramanian. "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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42

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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43

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 (December 2013): 5931–39. http://dx.doi.org/10.1109/tap.2013.2283599.

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44

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 (October 8, 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 (May 21, 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, Casey B. Hubbell, John D. Hedengren, Roar Nybø, and Manuel Aghito. "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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47

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 (July 23, 2020): 439–61. http://dx.doi.org/10.1007/s00158-020-02684-3.

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48

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 response surface for the Nusselt number. In particular, we combine previously developed experimental correlations (low-fidelity model) with direct numerical simulations (high-fidelity model) using Gaussian process regression and autoregressive stochastic schemes. In this framework the high-fidelity model is sampled only a few times, while the inexpensive empirical correlation is sampled at a very high rate. We obtain the mean Nusselt number directly from the stochastic multi-fidelity response surface, and we also propose an improved correlation. This new correlation seems to be consistent with the physics of this problem as we correct the vectorial addition of forced and natural convection with a pre-factor that weighs differently the forced convection. This, in turn, results in a new definition of the effective Reynolds number, hence accounting for the ‘incomplete similarity’ between mixed convection and forced convection. In addition, due to the probabilistic construction, we can quantify the uncertainty associated with the predictions. This information-fusion framework is useful for elucidating the physics of the flow, especially in cases where anomalous transport or interesting dynamics may be revealed by contrasting the variable fidelity across the models. While in this paper we focus on the thermal mixed convection, the multi-fidelity framework provides a new paradigm that could be used in many different contexts in fluid mechanics including heat and mass transport, but also in combining various levels of fidelity of models of turbulent flows.
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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 (June 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 of fidelity. In previous publications, the authors already proposed a high-fidelity multi-domain numerical model (HF), capable of accounting for a wide range of physical phenomena and progressive failures in the EMA, and a low-fidelity digital twin (LF). The LF is directly derived from the HF one by reducing the system degrees of freedom, simplifying the EMA control logic, eliminating the static inverter model and the three-phase commutation logic. In this work, the authors propose a new EMA digital twin, called Enhanced Low Fidelity (ELF), that, while still belonging to the simplified types, has particular characteristics that place it at an intermediate level of detail and accuracy between the HF and LF models. While maintaining a low computational cost, the ELF model keeps the original architecture of the three-phase motor and the multidomain approach typical of HF. The comparison of the preliminary results shows a satisfactory consistency between the experimental equipment and the numerical models.
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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 proposed. On this basis, the automatic extraction method of the above parameters is developed, and the key parameters of the method are recommended. In order to solve the above problems, taking the three typical Dou-Gong used in Liao Dynasty and Song Dynasty, including Zhutou Puzuo, Bujian Puzuo and Zhuanjiao Puzuo, as an example, briefly introduced the standardization characteristics of the typical components of the "Yingzao Fashi". Subsequently, the corresponding multiple LoD principles are recommended according to different requirements. Based on this and high-fidelity point cloud data, an automatic extraction method for multi-LoD BIM model parameters for typical components of wooden architectural heritage is proposed.</p>
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