Log in

Relevant bibliographies by topics / Multifidelity models / Journal articles

Journal articles on the topic 'Multifidelity models'

To see the other types of publications on this topic, follow the link: Multifidelity models.

Author: Grafiati

Published: 7 July 2024

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Multifidelity models.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Molléro, Roch, Xavier Pennec, Hervé Delingette, Alan Garny, Nicholas Ayache, and Maxime Sermesant. "Multifidelity-CMA: a multifidelity approach for efficient personalisation of 3D cardiac electromechanical models." Biomechanics and Modeling in Mechanobiology 17, no. 1 (September 11, 2017): 285–300. http://dx.doi.org/10.1007/s10237-017-0960-0.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Jacobs, Ryan, Philip E. Goins, and Dane Morgan. "Role of multifidelity data in sequential active learning materials discovery campaigns: case study of electronic bandgap." Machine Learning: Science and Technology 4, no. 4 (December 1, 2023): 045060. http://dx.doi.org/10.1088/2632-2153/ad1627.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Abstract Materials discovery and design typically proceeds through iterative evaluation (both experimental and computational) to obtain data, generally targeting improvement of one or more properties under one or more constraints (e.g. time or budget). However, there can be great variation in the quality and cost of different data, and when they are mixed together in what we here call multifidelity data, the optimal approaches to their utilization are not established. It is therefore important to develop strategies to acquire and use multifidelity data to realize the most efficient iterative materials exploration. In this work, we assess the impact of using multifidelity data through mock demonstration of designing solar cell materials, using the electronic bandgap as the target property. We propose a new approach of using multifidelity data through leveraging machine learning models of both low- and high-fidelity data, where using predicted low-fidelity data as an input feature in the high-fidelity model can improve the impact of a multifidelity data approach. We show how tradeoffs of low- versus high-fidelity measurement cost and acquisition can impact the materials discovery process. We find that the use of multifidelity data has maximal impact on the materials discovery campaign when approximately five low-fidelity measurements per high-fidelity measurement are performed, and when the cost of low-fidelity measurements is approximately 5% or less than that of high-fidelity measurements. This work provides practical guidance and useful qualitative measures for improving materials discovery campaigns that involve multifidelity data.

3

Narayan, Akil, Claude Gittelson, and Dongbin Xiu. "A Stochastic Collocation Algorithm with Multifidelity Models." SIAM Journal on Scientific Computing 36, no. 2 (January 2014): A495—A521. http://dx.doi.org/10.1137/130929461.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Peng, Yijie, Jie Xu, Loo Hay Lee, Jianqiang Hu, and Chun-Hung Chen. "Efficient Simulation Sampling Allocation Using Multifidelity Models." IEEE Transactions on Automatic Control 64, no. 8 (August 2019): 3156–69. http://dx.doi.org/10.1109/tac.2018.2886165.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Jasa, John, Pietro Bortolotti, Daniel Zalkind, and Garrett Barter. "Effectively using multifidelity optimization for wind turbine design." Wind Energy Science 7, no. 3 (May 11, 2022): 991–1006. http://dx.doi.org/10.5194/wes-7-991-2022.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Abstract. Wind turbines are complex multidisciplinary systems that are challenging to design because of the tightly coupled interactions between different subsystems. Computational modeling attempts to resolve these couplings so we can efficiently explore new wind turbine systems early in the design process. Low-fidelity models are computationally efficient but make assumptions and simplifications that limit the accuracy of design studies, whereas high-fidelity models capture more of the actual physics but with increased computational cost. This paper details the use of multifidelity methods for optimizing wind turbine designs by using information from both low- and high-fidelity models to find an optimal solution at reduced cost. Specifically, a trust-region approach is used with a novel corrective function built from a nonlinear surrogate model. We find that for a diverse set of design problems – with examples given in rotor blade geometry design, wind turbine controller design, and wind power plant layout optimization – the multifidelity method finds the optimal design using 38 %–58 % of the computational cost of the high-fidelity-only optimization. The success of the multifidelity method in disparate applications suggests that it could be more broadly applied to other wind energy or otherwise generic applications.

6

Rumpfkeil, Markus P., and Philip Beran. "Construction of Dynamic Multifidelity Locally Optimized Surrogate Models." AIAA Journal 55, no. 9 (September 2017): 3169–79. http://dx.doi.org/10.2514/1.j055834.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Zhu, Xueyu, Akil Narayan, and Dongbin Xiu. "Computational Aspects of Stochastic Collocation with Multifidelity Models." SIAM/ASA Journal on Uncertainty Quantification 2, no. 1 (January 2014): 444–63. http://dx.doi.org/10.1137/130949154.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Keshavarzzadeh, Vahid, Robert M. Kirby, and Akil Narayan. "Convergence Acceleration for Time-Dependent Parametric Multifidelity Models." SIAM Journal on Numerical Analysis 57, no. 3 (January 2019): 1344–68. http://dx.doi.org/10.1137/18m1170339.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Howard, Amanda, Yucheng Fu, and Panos Stinis. "A multifidelity approach to continual learning for physical systems." Machine Learning: Science and Technology 5, no. 2 (May 16, 2024): 025042. http://dx.doi.org/10.1088/2632-2153/ad45b2.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Abstract We introduce a novel continual learning method based on multifidelity deep neural networks. This method learns the correlation between the output of previously trained models and the desired output of the model on the current training dataset, limiting catastrophic forgetting. On its own the multifidelity continual learning method shows robust results that limit forgetting across several datasets. Additionally, we show that the multifidelity method can be combined with existing continual learning methods, including replay and memory aware synapses, to further limit catastrophic forgetting. The proposed continual learning method is especially suited for physical problems where the data satisfy the same physical laws on each domain, or for physics-informed neural networks, because in these cases we expect there to be a strong correlation between the output of the previous model and the model on the current training domain.

10

Pienaar, Elsje. "Multifidelity Analysis for Predicting Rare Events in Stochastic Computational Models of Complex Biological Systems." Biomedical Engineering and Computational Biology 9 (January 2018): 117959721879025. http://dx.doi.org/10.1177/1179597218790253.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Rare events such as genetic mutations or cell-cell interactions are important contributors to dynamics in complex biological systems, eg, in drug-resistant infections. Computational approaches can help analyze rare events that are difficult to study experimentally. However, analyzing the frequency and dynamics of rare events in computational models can also be challenging due to high computational resource demands, especially for high-fidelity stochastic computational models. To facilitate analysis of rare events in complex biological systems, we present a multifidelity analysis approach that uses medium-fidelity analysis (Monte Carlo simulations) and/or low-fidelity analysis (Markov chain models) to analyze high-fidelity stochastic model results. Medium-fidelity analysis can produce large numbers of possible rare event trajectories for a single high-fidelity model simulation. This allows prediction of both rare event dynamics and probability distributions at much lower frequencies than high-fidelity models. Low-fidelity analysis can calculate probability distributions for rare events over time for any frequency by updating the probabilities of the rare event state space after each discrete event of the high-fidelity model. To validate the approach, we apply multifidelity analysis to a high-fidelity model of tuberculosis disease. We validate the method against high-fidelity model results and illustrate the application of multifidelity analysis in predicting rare event trajectories, performing sensitivity analyses and extrapolating predictions to very low frequencies in complex systems. We believe that our approach will complement ongoing efforts to enable accurate prediction of rare event dynamics in high-fidelity computational models.

11

Ahmed, Shady E., Omer San, Kursat Kara, Rami Younis, and Adil Rasheed. "Multifidelity computing for coupling full and reduced order models." PLOS ONE 16, no. 2 (February 11, 2021): e0246092. http://dx.doi.org/10.1371/journal.pone.0246092.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Hybrid physics-machine learning models are increasingly being used in simulations of transport processes. Many complex multiphysics systems relevant to scientific and engineering applications include multiple spatiotemporal scales and comprise a multifidelity problem sharing an interface between various formulations or heterogeneous computational entities. To this end, we present a robust hybrid analysis and modeling approach combining a physics-based full order model (FOM) and a data-driven reduced order model (ROM) to form the building blocks of an integrated approach among mixed fidelity descriptions toward predictive digital twin technologies. At the interface, we introduce a long short-term memory network to bridge these high and low-fidelity models in various forms of interfacial error correction or prolongation. The proposed interface learning approaches are tested as a new way to address ROM-FOM coupling problems solving nonlinear advection-diffusion flow situations with a bifidelity setup that captures the essence of a broad class of transport processes.

12

Dhulipala, Somayajulu L. N., Wen Jiang, Benjamin W. Spencer, Jason D. Hales, Michael D. Shields, Andrew E. Slaughter, Zachary M. Prince, Vincent M. Labouré, Chandrakanth Bolisetti, and Promit Chakroborty. "Accelerated statistical failure analysis of multifidelity TRISO fuel models." Journal of Nuclear Materials 563 (May 2022): 153604. http://dx.doi.org/10.1016/j.jnucmat.2022.153604.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

Srivastava, Shobhit, and Nathan Michael. "Efficient, Multifidelity Perceptual Representations via Hierarchical Gaussian Mixture Models." IEEE Transactions on Robotics 35, no. 1 (February 2019): 248–60. http://dx.doi.org/10.1109/tro.2018.2878363.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

Vo, Huy D., Zachary Fox, Ania Baetica, and Brian Munsky. "Bayesian Estimation for Stochastic Gene Expression Using Multifidelity Models." Journal of Physical Chemistry B 123, no. 10 (February 19, 2019): 2217–34. http://dx.doi.org/10.1021/acs.jpcb.8b10946.

Full text

APA, Harvard, Vancouver, ISO, and other styles

15

Peherstorfer, Benjamin. "Multifidelity Monte Carlo Estimation with Adaptive Low-Fidelity Models." SIAM/ASA Journal on Uncertainty Quantification 7, no. 2 (January 2019): 579–603. http://dx.doi.org/10.1137/17m1159208.

Full text

APA, Harvard, Vancouver, ISO, and other styles

16

Du, Xiaosong, Jie Ren, and Leifur Leifsson. "Aerodynamic inverse design using multifidelity models and manifold mapping." Aerospace Science and Technology 85 (February 2019): 371–85. http://dx.doi.org/10.1016/j.ast.2018.12.008.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

Zeng, Xiaoshu, Gianluca Geraci, Michael S. Eldred, John D. Jakeman, Alex A. Gorodetsky, and Roger Ghanem. "Multifidelity uncertainty quantification with models based on dissimilar parameters." Computer Methods in Applied Mechanics and Engineering 415 (October 2023): 116205. http://dx.doi.org/10.1016/j.cma.2023.116205.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

Mu, Weiyan, Qiuyue Wei, Dongli Cui, and Shifeng Xiong. "Best Linear Unbiased Prediction for Multifidelity Computer Experiments." Mathematical Problems in Engineering 2018 (June 7, 2018): 1–7. http://dx.doi.org/10.1155/2018/8525736.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Recently it becomes a growing trend to study complex systems which contain multiple computer codes with different levels of accuracy, and a number of hierarchical Gaussian process models are proposed to handle such multiple-fidelity codes. This paper derives the best linear unbiased prediction for three popular classes of multiple-level Gaussian process models. The predictors all have explicit expressions at each untried point. Empirical best linear unbiased predictors are also provided by plug-in methods with generalized maximum likelihood estimators of unknown parameters.

19

Rumpfkeil, Markus P., and Philip S. Beran. "Multifidelity Sparse Polynomial Chaos Surrogate Models Applied to Flutter Databases." AIAA Journal 58, no. 3 (March 2020): 1292–303. http://dx.doi.org/10.2514/1.j058452.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Singh, Gulshan, and Ramana V. Grandhi. "Mixed-Variable Optimization Strategy Employing Multifidelity Simulation and Surrogate Models." AIAA Journal 48, no. 1 (January 2010): 215–23. http://dx.doi.org/10.2514/1.43469.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

Xing, W., M. Razi, R. M. Kirby, K. Sun, and A. A. Shah. "Greedy nonlinear autoregression for multifidelity computer models at different scales." Energy and AI 1 (August 2020): 100012. http://dx.doi.org/10.1016/j.egyai.2020.100012.

Full text

APA, Harvard, Vancouver, ISO, and other styles

22

Perry, Daniel J., Robert M. Kirby, Akil Narayan, and Ross T. Whitaker. "Allocation Strategies for High Fidelity Models in the Multifidelity Regime." SIAM/ASA Journal on Uncertainty Quantification 7, no. 1 (January 2019): 203–31. http://dx.doi.org/10.1137/17m1144714.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Yin, Faliang, Xiaoming Xue, Chengze Zhang, Kai Zhang, Jianfa Han, BingXuan Liu, Jian Wang, and Jun Yao. "Multifidelity Genetic Transfer: An Efficient Framework for Production Optimization." SPE Journal 26, no. 04 (January 21, 2021): 1614–35. http://dx.doi.org/10.2118/205013-pa.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Summary Production optimization led by computing intelligence can greatly improve oilfield economic effectiveness. However, it is confronted with huge computational challenge because of the expensive black-box objective function and the high-dimensional design variables. Many low-fidelity methods based on simplified physical models or data-driven models have been proposed to reduce evaluation costs. These methods can approximate the global fitness landscape to a certain extent, but it is difficult to ensure accuracy and correlation in local areas. Multifidelity methods have been proposed to balance the advantages of the two, but most of the current methods rely on complex computational models. Through a simple but efficient shortcut, our work aims to establish a novel production-optimization framework using genetic transfer learning to accelerate convergence and improve the quality of optimal solution using results from different fidelities. Net present value (NPV) is a widely used standard to comprehensively evaluate the economic value of a strategy in production optimization. On the basis of NPV, we first established a multifidelity optimization model that can synthesize the reference information from high-fidelity tasks and the approximate results from low-fidelity tasks. Then, we introduce the concept of relative fidelity as an indicator for quantifying the dynamic reliability of low-fidelity methods, and further propose a two-mode multifidelity genetic transfer learning framework that balances computing resources for tasks with different fidelity levels. The multitasking mode takes the elite solution as the transfer medium and forms a closed-loop feedback system through the information exchange between low- and high-fidelity tasks in parallel. Sequential transfer mode, a one-way algorithm, transfers the elite solutions archived in the previous mode as the population to high-fidelity domain for further optimization. This framework is suitable for population-based optimization algorithms with variable search direction and step size. The core work of this paper is to realize the framework by means of differential evolution (DE), for which we propose the multifidelity transfer differential evolution (MTDE). Corresponding to multitasking and sequential transfer in the framework, MTDE includes two modes, transfer based on base vector (b-transfer) and transfer based on population (p-transfer). The b-transfer mode incorporates the unique advantages of DE into fidelity switching, whereas the p-transfer mode adaptively conducts population for further high-fidelity local search. Finally, the production-optimization performance of MTDE is validated with the egg model and two real field cases, in which the black-oil and streamline models are used to obtain high- and low-fidelity results, respectively. We also compared the convergence curves and optimization results with the single-fidelity method and the greedy multifidelity method. The results show that the proposed algorithm has a faster convergence rate and a higher-quality well-control strategy. The adaptive capacity of p-transfer is also demonstrated in three distinct cases. At the end of the paper, we discuss the generalization potential of the proposed framework.

24

Shi, Yan, Zhiqiang Wan, Zhigang Wu, and Chao Yang. "Nonlinear Unsteady Aerodynamics Reduced Order Model of Airfoils Based on Algorithm Fusion and Multifidelity Framework." International Journal of Aerospace Engineering 2021 (September 16, 2021): 1–26. http://dx.doi.org/10.1155/2021/4368104.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

A reduced order modeling method based on algorithm fusion and multifidelity framework for nonlinear unsteady aerodynamics is proposed to obtain a low-cost and high-precision unsteady aerodynamic model. This method integrates the traditional algorithm, intelligent algorithm, and multifidelity data fusion algorithm. In this method, the traditional algorithm is based on separated flow theory, the intelligent algorithm refers to the nonlinear autoregressive (NARX) method, and the multifidelity data fusion algorithm uses different fidelity data for aerodynamic modeling, which can shorten the time cost of data acquisition. In the process of modeling, firstly, a multifidelity model with NARX description provides a general intelligent algorithm framework for unsteady aerodynamics. Then, based on the separated flow theory, the correction equation from low-fidelity model to high-fidelity result is constructed, and the cuckoo algorithm based on chaos optimization is used to identify the parameters. In order to verify the effectiveness of the method, an unsteady aerodynamic model of NACA0012 airfoil is established. Three kinds of data with low, medium, and high fidelity are used for modeling. The low-fidelity and medium-fidelity data is obtained from the CFD-Euler solver and CFD-RANS solver, respectively, while the high-fidelity data comes from the experimental results. Then, the model is established, and its prediction of unsteady aerodynamic coefficients is in good agreement with the CFD results and the experimental data. After that, the model is applied to a two-dimensional aeroelastic system, and the bifurcation and limit cycle response analysis are compared with the experimental results, which further shows that the model can accurately capture the main flow characteristics in the flow range of low speed and high angle of attack. In addition, the convergence of the model is studied; the accuracy and generalization ability as well as applicability scope of the model are compared with other aerodynamic models and finally discussed.

25

Seo, Jongmin, Casey Fleeter, Andrew M. Kahn, Alison L. Marsden, and Daniele E. Schiavazzi. "MULTIFIDELITY ESTIMATORS FOR CORONARY CIRCULATION MODELS UNDER CLINICALLY INFORMED DATA UNCERTAINTY." International Journal for Uncertainty Quantification 10, no. 5 (2020): 449–66. http://dx.doi.org/10.1615/int.j.uncertaintyquantification.2020033068.

Full text

APA, Harvard, Vancouver, ISO, and other styles

26

Roderick, Oleg, Mihai Anitescu, and Yulia Peet. "Proper orthogonal decompositions in multifidelity uncertainty quantification of complex simulation models." International Journal of Computer Mathematics 91, no. 4 (January 20, 2014): 748–69. http://dx.doi.org/10.1080/00207160.2013.844431.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Sinsbeck, Michael, and Daniel M. Tartakovsky. "Impact of Data Assimilation on Cost-Accuracy Tradeoff in Multifidelity Models." SIAM/ASA Journal on Uncertainty Quantification 3, no. 1 (January 2015): 954–68. http://dx.doi.org/10.1137/141001743.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

Nagawkar, Jethro, Jie Ren, Xiaosong Du, Leifur Leifsson, and Slawomir Koziel. "Single- and Multipoint Aerodynamic Shape Optimization Using Multifidelity Models and Manifold Mapping." Journal of Aircraft 58, no. 3 (May 2021): 591–608. http://dx.doi.org/10.2514/1.c035297.

Full text

APA, Harvard, Vancouver, ISO, and other styles

29

Amrit, Anand, Leifur Leifsson, and Slawomir Koziel. "Fast Multi-Objective Aerodynamic Optimization Using Sequential Domain Patching and Multifidelity Models." Journal of Aircraft 57, no. 3 (May 2020): 388–98. http://dx.doi.org/10.2514/1.c035500.

Full text

APA, Harvard, Vancouver, ISO, and other styles

30

Clare, Mariana C. A., Tim W. B. Leijnse, Robert T. McCall, Ferdinand L. M. Diermanse, Colin J. Cotter, and Matthew D. Piggott. "Multilevel multifidelity Monte Carlo methods for assessing uncertainty in coastal flooding." Natural Hazards and Earth System Sciences 22, no. 8 (August 3, 2022): 2491–515. http://dx.doi.org/10.5194/nhess-22-2491-2022.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Abstract. When choosing an appropriate hydrodynamic model, there is always a compromise between accuracy and computational cost, with high-fidelity models being more expensive than low-fidelity ones. However, when assessing uncertainty, we can use a multifidelity approach to take advantage of the accuracy of high-fidelity models and the computational efficiency of low-fidelity models. Here, we apply the multilevel multifidelity Monte Carlo method (MLMF) to quantify uncertainty by computing statistical estimators of key output variables with respect to uncertain input data, using the high-fidelity hydrodynamic model XBeach and the lower-fidelity coastal flooding model SFINCS (Super-Fast INundation of CoastS). The multilevel aspect opens up the further advantageous possibility of applying each of these models at multiple resolutions. This work represents the first application of MLMF in the coastal zone and one of its first applications in any field. For both idealised and real-world test cases, MLMF can significantly reduce computational cost for the same accuracy compared to both the standard Monte Carlo method and to a multilevel approach utilising only a single model (the multilevel Monte Carlo method). In particular, here we demonstrate using the case of Myrtle Beach, South Carolina, USA, that this improvement in computational efficiency allows for in-depth uncertainty analysis to be conducted in the case of real-world coastal environments – a task that would previously have been practically unfeasible. Moreover, for the first time, we show how an inverse transform sampling technique can be used to accurately estimate the cumulative distribution function (CDF) of variables from the MLMF outputs. MLMF-based estimates of the expectations and the CDFs of the variables of interest are of significant value to decision makers when assessing uncertainty in predictions.

31

Robinson, T. D., M. S. Eldred, K. E. Willcox, and R. Haimes. "Surrogate-Based Optimization Using Multifidelity Models with Variable Parameterization and Corrected Space Mapping." AIAA Journal 46, no. 11 (November 2008): 2814–22. http://dx.doi.org/10.2514/1.36043.

Full text

APA, Harvard, Vancouver, ISO, and other styles

32

Chan, F. T. S., A. Chaube, V. Mohan, V. Arora, and M. K. Tiwari. "Operation allocation in automated manufacturing system using GA-based approach with multifidelity models." Robotics and Computer-Integrated Manufacturing 26, no. 5 (October 2010): 526–34. http://dx.doi.org/10.1016/j.rcim.2010.04.002.

Full text

APA, Harvard, Vancouver, ISO, and other styles

33

Allaire, Douglas, and Karen Willcox. "A MATHEMATICAL AND COMPUTATIONAL FRAMEWORK FOR MULTIFIDELITY DESIGN AND ANALYSIS WITH COMPUTER MODELS." International Journal for Uncertainty Quantification 4, no. 1 (2014): 1–20. http://dx.doi.org/10.1615/int.j.uncertaintyquantification.2013004121.

Full text

APA, Harvard, Vancouver, ISO, and other styles

34

Kontaxoglou, Anastasios, Seiji Tsutsumi, Samir Khan, and Shinichi Nakasuka. "Towards a Digital Twin Enabled Multifidelity Framework for Small Satellites." PHM Society European Conference 6, no. 1 (June 29, 2021): 10. http://dx.doi.org/10.36001/phme.2021.v6i1.2801.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

In this work, a multi-fidelity framework for the simulation ofsmall satellites is investigated. Taking into account the conceptof digital twin, our work focuses on handling a constantstream of live data. Towards this end, current multi-fidelitymodelling methods and low fidelity surrogate models for timeseries were surveyed. A multi-fidelity approach is used tocombine a low fidelity surrogate model with a high fidelitymodel. As a high fidelity model, a previously investigatedfinite element model is assumed. As a low fidelity model,auto-regressive and recurrent neural network-based modelsare investigated. Through cokriging, the low fidelity data iscorrected by the high fidelity data through a comprehensivecorrection, where the parameters are given through Gaussianprocesses in order to perform uncertainty quantification. Asan application, the thermal simulation of a small satellite, andthe use of this framework in conjunction with sparse telemetrydata is proposed. This online, statistical approach aims toprovide a tool for performing fault detection.

35

Messina, Luca, Alessio Quaglino, Alexandra Goryaeva, Mihai-Cosmin Marinica, Christophe Domain, Nicolas Castin, Giovanni Bonny, and Rolf Krause. "A DFT-driven multifidelity framework for constructing efficient energy models for atomic-scale simulations." Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 483 (November 2020): 15–21. http://dx.doi.org/10.1016/j.nimb.2020.09.011.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

Gruber, Anthony, Max Gunzburger, Lili Ju, Rihui Lan, and Zhu Wang. "Multifidelity Monte Carlo estimation for efficient uncertainty quantification in climate-related modeling." Geoscientific Model Development 16, no. 4 (February 21, 2023): 1213–29. http://dx.doi.org/10.5194/gmd-16-1213-2023.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Abstract. Uncertainties in an output of interest that depends on the solution of a complex system (e.g., of partial differential equations with random inputs) are often, if not nearly ubiquitously, determined in practice using Monte Carlo (MC) estimation. While simple to implement, MC estimation fails to provide reliable information about statistical quantities (such as the expected value of the output of interest) in application settings such as climate modeling, for which obtaining a single realization of the output of interest is a costly endeavor. Specifically, the dilemma encountered is that many samples of the output of interest have to be collected in order to obtain an MC estimator that has sufficient accuracy – so many, in fact, that the available computational budget is not large enough to effect the number of samples needed. To circumvent this dilemma, we consider using multifidelity Monte Carlo (MFMC) estimation which leverages the use of less costly and less accurate surrogate models (such as coarser grids, reduced-order models, simplified physics, and/or interpolants) to achieve, for the same computational budget, higher accuracy compared to that obtained by an MC estimator – or, looking at it another way, an MFMC estimator obtains the same accuracy as the MC estimator at lower computational cost. The key to the efficacy of MFMC estimation is the fact that most of the required computational budget is loaded onto the less costly surrogate models so that very few samples are taken of the more expensive model of interest. We first provide a more detailed discussion about the need to consider an alternative to MC estimation for uncertainty quantification. Subsequently, we present a review, in an abstract setting, of the MFMC approach along with its application to three climate-related benchmark problems as a proof-of-concept exercise.

37

Berci, Marco, and Francesco Torrigiani. "Multifidelity Sensitivity Study of Subsonic Wing Flutter for Hybrid Approaches in Aircraft Multidisciplinary Design and Optimisation." Aerospace 7, no. 11 (November 12, 2020): 161. http://dx.doi.org/10.3390/aerospace7110161.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

A comparative sensitivity study for the flutter instability of aircraft wings in subsonic flow is presented, using analytical models and numerical tools with different multidisciplinary approaches. The analyses build on previous elegant works and encompass parametric variations of aero-structural properties, quantifying their effect on the aeroelastic stability boundary. Differences in the multifidelity results are critically assessed from both theoretical and computational perspectives, in view of possible practical applications within airplane preliminary design and optimisation. A robust hybrid strategy is then recommended, wherein the flutter boundary is obtained using a higher-fidelity approach while the flutter sensitivity is computed adopting a lower-fidelity approach.

38

Hebert, James L., Thomas H. Holzer, Timothy J. Eveleigh, and Shahryar Sarkani. "Use of Multifidelity and Surrogate Models in the Design and Development of Physics-Based Systems." Systems Engineering 19, no. 4 (July 2016): 375–91. http://dx.doi.org/10.1002/sys.21346.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

Xu, C., Z. Liu, B. T. Cao, G. Meschke, and X. Liu. "Multifidelity operator learning for predicting displacement fields of tunnel linings under external loads." IOP Conference Series: Earth and Environmental Science 1333, no. 1 (May 1, 2024): 012045. http://dx.doi.org/10.1088/1755-1315/1333/1/012045.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Abstract In practical tunnel projects, the deformation of tunnel linings affects the service performance and structural reliability of tunnels. However, currently, several monitoring points must be installed to represent the specific deformation patterns of tunnel linings accurately, incurring high labor and financial costs. In recent years, the rapid development of artificial intelligence has provided potential solutions to this issue. To solve the aforementioned problem, this study proposed a multifidelity DeepONet framework, which comprised two neural networks. The first low-fidelity network was trained with data provided by a macro-level numerical model validated by experimental campaigns to learn the physical deformation patterns of tunnel linings. The second network was trained with limited high-fidelity monitoring data to learn the correlations between observations and numerical models. Even with very limited monitoring data, the proposed framework could still predict the mechanical behavior of tunnel linings under different loading scenarios. In this study, data collected from noncircular tunnel projects were used as case studies. The results demonstrated that the final output conformed to the deformation pattern obtained with the numerical simulations and was consistent with the actual measurements, achieving seamless fusion of the experimental campaigns and numerical models.

40

Abraham, Troy, David Lazzara, and Douglas Hunsaker. "Multifidelity Comparison of Supersonic Wave Drag Prediction Methods Using Axisymmetric Bodies." Aerospace 11, no. 5 (April 30, 2024): 359. http://dx.doi.org/10.3390/aerospace11050359.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Low-fidelity analytic and computational wave drag prediction methods assume linear aerodynamics and small perturbations to the flow. Hence, these methods are typically accurate for only very slender geometries. The present work assesses the accuracy of these methods relative to high-fidelity Euler, compressible computational-fluid-dynamics solutions for a set of axisymmetric geometries with varying radius-to-length ratios (R/L). Grid-resolution studies are included for all computational results to ensure grid-resolved results. Results show that the low-fidelity analytic and computational methods match the Euler CFD predictions to around a single drag count ( ∼1.0×10−4) for geometries with R/L≤0.05 and Mach numbers from 1.1 to 2.0. The difference in predicted wave drag rapidly increases, to over 30 drag counts in some cases, for geometries approaching R/L≈0.1, indicating that the slender-body assumption of linear supersonic theory is violated for larger radius-to-length ratios. All three methods considered predict that the wave drag coefficient is nearly independent of Mach number for the geometries included in this study. Results of the study can be used to validate other numerical models and estimate the error in low-fidelity analytic and computational methods for predicting wave drag of axisymmetric geometries, depending on radius-to-length ratios.

41

Jiang, Zhenxiang, Jongeun Choi, and Seungik Baek. "Machine learning approaches to surrogate multifidelity Growth and Remodeling models for efficient abdominal aortic aneurysmal applications." Computers in Biology and Medicine 133 (June 2021): 104394. http://dx.doi.org/10.1016/j.compbiomed.2021.104394.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Berends, K. D., F. Scheel, J. J. Warmink, W. P. de Boer, R. Ranasinghe, and S. J. M. H. Hulscher. "Towards efficient uncertainty quantification with high-resolution morphodynamic models: A multifidelity approach applied to channel sedimentation." Coastal Engineering 152 (October 2019): 103520. http://dx.doi.org/10.1016/j.coastaleng.2019.103520.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

Berci, M., P. H. Gaskell, R. W. Hewson, and V. V. Toropov. "Multifidelity metamodel building as a route to aeroelastic optimization of flexible wings." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 225, no. 9 (July 5, 2011): 2115–37. http://dx.doi.org/10.1177/0954406211403549.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

High-fidelity aeroelastic simulations of the response of flexible wings to a sudden gust can result in a huge computing effort, making the search for the best wing design prohibitively expensive. As an alternative, a cost-effective multifidelity metamodelling-based optimization strategy, where a metamodel of a high-fidelity aeroelastic simulation response is built by tuning a lower fidelity aeroelastic simulation response, is proposed. In order to address and validate such an approach, both linear and non-linear aeroelastic equations for an aerofoil employing different levels of complexity for expressing the aerodynamic load are used for the high- and low-fidelity models. An aeroelastic gust response evaluation problem is formulated for the flexible wing of a small unmanned air vehicle, whose characteristic size makes it particularly susceptible to gusts. Three different approaches to tune the low-fidelity model, both explicit and implicit, are investigated and compared. Good agreement between the high-fidelity model and the corrected low-fidelity one shows that the proposed approach is indeed suitable for optimization of the aeroelastic gust performance of flexible wings.

44

Reed, John A., and Abdollah A. Afjeh. "Computational Simulation of Gas Turbines: Part 1—Foundations of Component-Based Models." Journal of Engineering for Gas Turbines and Power 122, no. 3 (May 15, 2000): 366–76. http://dx.doi.org/10.1115/1.1287490.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Designing and developing new aerospace propulsion systems is time-consuming and expensive. Computational simulation is a promising means for alleviating this cost, but requires a flexible software simulation system capable of integrating advanced multidisciplinary and multifidelity analysis methods, dynamically constructing arbitrary simulation models, and distributing computationally complex tasks. To address these issues, we have developed Onyx, a Java-based object-oriented domain framework for aerospace propulsion system simulation. This paper presents the design of a common engineering model formalism for use in Onyx. This approach, which is based on hierarchical decomposition and standardized interfaces, provides a flexible component-based representation for gas turbine systems, subsystems and components. It allows new models to be composed programmatically or visually to form more complex models. Onyx’s common engineering model also supports integration of a hierarchy of models which represent the system at differing levels of abstraction. Selection of a particular model is based on a number of criteria, including the level of detail needed, the objective of the simulation, the available knowledge, and given resources. The common engineering model approach is demonstrated by developing gas turbine component models which will be used to compose a gas turbine engine model in Part 2 of this paper. [S0742-4795(00)02303-6]

45

Nguyen, Vinh-Tan, Jason Yu Chuan Leong, Satoshi Watanabe, Toshimitsu Morooka, and Takayuki Shimizu. "A Multi-Fidelity Model for Simulations and Sensitivity Analysis of Piezoelectric Inkjet Printheads." Micromachines 12, no. 9 (August 29, 2021): 1038. http://dx.doi.org/10.3390/mi12091038.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

The ink drop generation process in piezoelectric droplet-on-demand devices is a complex multiphysics process. A fully resolved simulation of such a system involves a coupled fluid–structure interaction approach employing both computational fluid dynamics (CFD) and computational structural mechanics (CSM) models; thus, it is computationally expensive for engineering design and analysis. In this work, a simplified lumped element model (LEM) is proposed for the simulation of piezoelectric inkjet printheads using the analogy of equivalent electrical circuits. The model’s parameters are computed from three-dimensional fluid and structural simulations, taking into account the detailed geometrical features of the inkjet printhead. Inherently, this multifidelity LEM approach is much faster in simulations of the whole inkjet printhead, while it ably captures fundamental electro-mechanical coupling effects. The approach is validated with experimental data for an existing commercial inkjet printhead with good agreement in droplet speed prediction and frequency responses. The sensitivity analysis of droplet generation conducted for the variation of ink channel geometrical parameters shows the importance of different design variables on the performance of inkjet printheads. It further illustrates the effectiveness of the proposed approach in practical engineering usage.

46

Marques, Simão, Lucas Kob, Trevor T. Robinson, and Weigang Yao. "Nonintrusive Aerodynamic Shape Optimisation with a POD-DEIM Based Trust Region Method." Aerospace 10, no. 5 (May 17, 2023): 470. http://dx.doi.org/10.3390/aerospace10050470.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

This work presents a strategy to build reduced-order models suitable for aerodynamic shape optimisation, resulting in a multifidelity optimisation framework. A reduced-order model (ROM) based on a discrete empirical interpolation (DEIM) method is employed in lieu of computational fluid dynamics (CFD) solvers for fast, nonlinear, aerodynamic modelling. The DEIM builds a set of interpolation points that allows it to reconstruct the flow fields from sets of basis obtained by proper orthogonal decomposition of a matrix of snapshots. The aerodynamic reduced-order model is completed by introducing a nonlinear mapping function between surface deformation and the DEIM interpolation points. The optimisation problem is managed by a trust region algorithm linking the multiple-fidelity solvers, with each subproblem solved using a gradient-based algorithm. The design space is initially restricted; as the optimisation trajectory evolves, new samples enrich the ROM. The proposed methodology is evaluated using a series of transonic viscous test cases based on wing configurations. Results show that for cases with a moderate number of design variables, the approach proposed is competitive with state-of-the-art gradient-based methods; in addition, the use of trust region methodology mitigates the likelihood of the optimiser converging to, shallower, local minima.

47

Carpenter, Chris. "Digital-Twin Approach Predicts Fatigue Damage of Marine Risers." Journal of Petroleum Technology 73, no. 10 (October 1, 2021): 65–66. http://dx.doi.org/10.2118/1021-0065-jpt.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 30985, “From Data to Assessment Models, Demonstrated Through a Digital Twin of Marine Risers,” by Ehsan Kharazmi and Zhicheng Wang, Brown University, and Dixia Fan, SPE, Massachusetts Institute of Technology, et al., prepared for the 2021 Offshore Technology Conference, Houston, 16–19 August. The paper has not been peer reviewed. Copyright 2021 Offshore Technology Conference. Reproduced by permission. Assessing fatigue damage in marine risers caused by vortex-induced vibrations (VIV) serves as a comprehensive example of using machine-learning methods to derive assessment models of complex systems. A complete characterization of the response of such complex systems usually is unavailable despite massive experimental data and computation results. These algorithms can use multifidelity data sets from multiple sources. In the complete paper, the authors develop a three-pronged approach to demonstrate how tools in machine learning are used to develop data-driven models that can be used for accurate and efficient fatigue-damage predictions for marine risers subject to VIV. Introduction In this study, machine-learning tools are developed to construct a digital twin of a marine riser. The digital twin uses various sources of training data, including field data, experimental data, computational-fluid-dynamics simulations, extracted databases, semiempirical codes, and existing knowledge of underlying physical models. The authors also show that a well-trained digital twin can use the streaming data from a few field sensors efficiently to provide an accurate reconstruction of motion and to provide fatigue-damage prediction. Several machine-learning algorithms have been developed in the literature to predict the life span of the structure through the changes in parameters. To the best of the authors’ knowledge, most existing methods are developed as black boxes that return parameters by only feeding experimental data and therefore are ignorant of the underlying physics. In the first of three approaches, the authors enhance the capabilities of semiempirical codes by developing efficient databases through active learning. In the second approach, the LSTM-ModNet framework is applied to reconstruct and analyze the entire motion of a riser in deep water from sensor measurements through modal decomposition in space and the sequence-learning capability of recurrent neural networks in time. The formulation described in the paper provides a tool that efficiently combines different types of sensor measurements, such as strain and acceleration. In the third approach, a higher level of abstraction is introduced and the nonlinear operator that maps the inflow current velocity to the root-mean-square function of the riser response is approximated. In particular, the newly developed neural network DeepONet is used as a black box to learn the mapping between the input parameters (the inflow velocity, riser bending stiffness, and tension as a function of water depth) to the output parameters (strain, amplitude, and exciting frequencies as a function of water depth). In these approaches, data from the high-mode VIV test is used to train the networks.

48

Rehme, Michael, Stephen Roberts, and Dirk Pflüger. "Uncertainty quantification for the Hokkaido Nansei-Oki tsunami using B-splines on adaptive sparse grids." ANZIAM Journal 62 (June 29, 2021): C30—C44. http://dx.doi.org/10.21914/anziamj.v62.16121.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Modeling uncertainties in the input parameters of computer simulations is an established way to account for inevitably limited knowledge. To overcome long run-times and high demand for computational resources, a surrogate model can replace the original simulation. We use spatially adaptive sparse grids for the creation of this surrogate model. Sparse grids are a discretization scheme designed to mitigate the curse of dimensionality, and spatial adaptivity further decreases the necessary number of expensive simulations. We combine this with B-spline basis functions which provide gradients and are exactly integrable. We demonstrate the capability of this uncertainty quantification approach for a simulation of the Hokkaido Nansei–Oki Tsunami with anuga. We develop a better understanding of the tsunami behavior by calculating key quantities such as mean, percentiles and maximum run-up. We compare our approach to the popular Dakota toolbox and reach slightly better results for all quantities of interest. References B. M. Adams, M. S. Ebeida, et al. Dakota. Sandia Technical Report, SAND2014-4633, Version 6.11 User’s Manual, July 2014. 2019. https://dakota.sandia.gov/content/manuals. J. H. S. de Baar and S. G. Roberts. Multifidelity sparse-grid-based uncertainty quantification for the Hokkaido Nansei–Oki tsunami. Pure Appl. Geophys. 174 (2017), pp. 3107–3121. doi: 10.1007/s00024-017-1606-y. H.-J. Bungartz and M. Griebel. Sparse grids. Acta Numer. 13 (2004), pp. 147–269. doi: 10.1017/S0962492904000182. M. Eldred and J. Burkardt. Comparison of non-intrusive polynomial chaos and stochastic collocation methods for uncertainty quantification. 47th AIAA. 2009. doi: 10.2514/6.2009-976. K. Höllig and J. Hörner. Approximation and modeling with B-splines. Philadelphia: SIAM, 2013. doi: 10.1137/1.9781611972955. M. Matsuyama and H. Tanaka. An experimental study of the highest run-up height in the 1993 Hokkaido Nansei–Oki earthquake tsunami. National Tsunami Hazard Mitigation Program Review and International Tsunami Symposium (ITS). 2001. O. Nielsen, S. Roberts, D. Gray, A. McPherson, and A. Hitchman. Hydrodymamic modelling of coastal inundation. MODSIM 2005. 2005, pp. 518–523. https://www.mssanz.org.au/modsim05/papers/nielsen.pdf. J. Nocedal and S. J. Wright. Numerical optimization. Springer, 2006. doi: 10.1007/978-0-387-40065-5. D. Pflüger. Spatially Adaptive Sparse Grids for High-Dimensional Problems. Dr. rer. nat., Technische Universität München, Aug. 2010. https://www5.in.tum.de/pub/pflueger10spatially.pdf. M. F. Rehme, F. Franzelin, and D. Pflüger. B-splines on sparse grids for surrogates in uncertainty quantification. Reliab. Eng. Sys. Saf. 209 (2021), p. 107430. doi: 10.1016/j.ress.2021.107430. M. F. Rehme and D. Pflüger. Stochastic collocation with hierarchical extended B-splines on Sparse Grids. Approximation Theory XVI, AT 2019. Springer Proc. Math. Stats. Vol. 336. Springer, 2020. doi: 10.1007/978-3-030-57464-2_12. S Roberts, O. Nielsen, D. Gray, J. Sexton, and G. Davies. ANUGA. Geoscience Australia. 2015. doi: 10.13140/RG.2.2.12401.99686. I. J. Schoenberg and A. Whitney. On Pólya frequence functions. III. The positivity of translation determinants with an application to the interpolation problem by spline curves. Trans. Am. Math. Soc. 74.2 (1953), pp. 246–259. doi: 10.2307/1990881. W. Sickel and T. Ullrich. Spline interpolation on sparse grids. Appl. Anal. 90.3–4 (2011), pp. 337–383. doi: 10.1080/00036811.2010.495336. C. E. Synolakis, E. N. Bernard, V. V. Titov, U. Kânoğlu, and F. I. González. Standards, criteria, and procedures for NOAA evaluation of tsunami numerical models. NOAA/Pacific Marine Environmental Laboratory. 2007. https://nctr.pmel.noaa.gov/benchmark/. J. Valentin and D. Pflüger. Hierarchical gradient-based optimization with B-splines on sparse grids. Sparse Grids and Applications—Stuttgart 2014. Lecture Notes in Computational Science and Engineering. Vol. 109. Springer, 2016, pp. 315–336. doi: 10.1007/978-3-319-28262-6_13. D. Xiu and G. E. Karniadakis. The Wiener–Askey polynomial chaos for stochastic differential equations. SIAM J. Sci. Comput. 24.2 (2002), pp. 619–644. doi: 10.1137/S1064827501387826.

49

Adjei, Richard Amankwa, Xinqian Zheng, Fangyuan Lou, and Chuang Ding. "Multifidelity Optimization Under Uncertainty for Robust Design of a Micro-Turbofan Turbine Stage." Journal of Engineering for Gas Turbines and Power, August 16, 2022. http://dx.doi.org/10.1115/1.4055231.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Abstract This paper presents a multifidelity optimization strategy for efficient uncertainty quantification and robust optimization applicable to turbomachinery blade design. The proposed strategy leverages freeform parameterization technique for flexible geometric perturbation and multifidelity information to reduce the number of evaluations of the expensive information source needed for robust optimization. The multifidelity Monte Carlo method was used to construct and exploit a surrogate-based multifidelity model based on the combination of high and low-fidelity CFD simulations and cheap regression models. Uncertainty quantification and robust optimization considering manufacturing tolerances were performed at a single operating point. An improvement in mean isentropic expansion efficiency of 2.98% was achieved for the robust design compared with the baseline although the mean mass flow rate and total pressure ratio differed by 1.72% and 0.67% respectively. Compared to a single high fidelity model, the multifidelity model was able to estimate the mean with a maximum deviation of 0.28% and 2.9% for the standard deviation. Furthermore, the multifidelity model realized a percentage reduction in computational cost of 66.18% for a combination of high fidelity CFD and regression models and 17.87% for high and low CFD models. One key observation was that, for small sampled high fidelity CFD datasets that are highly correlated, it is possible to use only the high fidelity model combined with regression models for constructing the multifidelity model without the need for low fidelity CFD dataset. This significantly reduces computational cost and time.

50

Kontaxoglou, Anastasios, Seiji Tsutsumi, Samir Khan, and Shinichi Nakasuka. "Multifidelity Framework for Small Satellite Thermal Analysis." Journal of Spacecraft and Rockets, September 13, 2023, 1–11. http://dx.doi.org/10.2514/1.a35666.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Anomalies, unexpected events, and model inaccuracies have detrimental effects on satellite operations. High-fidelity models are required, but these models quickly become large and expensive. Cheap or low-fidelity models speed up computation but lack accuracy. To compromise these requirements, this study proposes a multifidelity framework based on cokriging. The proposed multifidelity framework is compared against three other standard methods often used in satellite simulations: a standalone gated recurrent unit, Gaussian process regression, and the autoregressive integrated moving average with explanatory variables model. The robustness of high-fidelity data point placement is also examined. Moreover, the real-time aspect of the simulation is considered by applying the sliding window technique. This multifidelity framework is demonstrated using temperature data obtained from thermal vacuum testing of Small Demonstration Satellite 4: a 50-kg-class satellite. The multifidelity framework provided higher accuracy and robustness than the other methods, however, having a higher computational cost as compared to a purely low-fidelity model. Up to 92% reduction of the error was achieved by the proposed framework.