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Artículos de revistas sobre el tema "Surrogate dynamics"

Autor: Grafiati

Publicado: 30 de noviembre de 2024

Última modificación: 31 de julio de 2025

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1

Varsha D. Vyas. "Investigating the Commercial Surrogacy Sector in Mumbai: Trends, Challenges, and Dynamics." Journal of Information Systems Engineering and Management 10, no. 42s (2025): 1124–37. https://doi.org/10.52783/jisem.v10i42s.8265.

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The commercial surrogacy sector in Mumbai has emerged as a complex and controversial phenomenon within the realm of assisted reproductive technologies (ART), driven by rising infertility rates, changing societal norms, and the increasing acceptance of alternative family structures. Despite India's relatively low medical costs and advanced healthcare infrastructure, the industry faces significant challenges, including a lack of comprehensive legal protections for surrogate mothers and intended parents. Ethical dilemmas arise as many surrogate mothers enter agreements out of financial necessity,

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2

Huang, C.-K., Q. Tang, Y. K. Batygin, et al. "Symplectic neural surrogate models for beam dynamics." Journal of Physics: Conference Series 2687, no. 6 (2024): 062026. http://dx.doi.org/10.1088/1742-6596/2687/6/062026.

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Abstract Development of robust machine-learning (ML) based surrogates for particle accelerators can significantly benefit the modeling, design, optimization, monitoring and control of such accelerators. It is desirable that the surrogate models embed fundamental physical constraints to the interaction and dynamics of the beams, for which an accelerator must be designed to operate upon. We implement and train a class of phase space structure-preserving neural networks — Henon Neural Networks (HenonNets) [1], for nonlinear beam dynamics problems. It is demonstrated that the trained HenonNet mode

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3

NAKAMURA, TOMOMICHI, and MICHAEL SMALL. "APPLYING THE METHOD OF SMALL–SHUFFLE SURROGATE DATA: TESTING FOR DYNAMICS IN FLUCTUATING DATA WITH TRENDS." International Journal of Bifurcation and Chaos 16, no. 12 (2006): 3581–603. http://dx.doi.org/10.1142/s0218127406016999.

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Recently, a new surrogate method, the Small–Shuffle (SS) surrogate method, has been proposed to investigate whether there is some kind of dynamics in irregular fluctuations, even if they are modulated by long term trends or periodicities. This situation is theoretically incompatible with the assumption underlying previously proposed surrogate methods. We apply the SS surrogate method to a variety of simulated data with known dynamics and actual time series with unknown dynamics.

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4

Koutsoupakis, Josef, and Dimitrios Giagopoulos. "Drivetrain Response Prediction Using AI-based Surrogate and Multibody Dynamics Model." Machines 11, no. 5 (2023): 514. http://dx.doi.org/10.3390/machines11050514.

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Numerical models, such as multibody dynamics ones, are broadly used in various engineering applications, either as an integral part of the preliminary design of a product or simply to analyze its behavior. Aiming to increase the accuracy and potential of these models, complex mechanisms are constantly being added to existing methods of simulation, leading to powerful modelling frameworks that are able to simulate most mechanical systems. This increase in accuracy and flexibility, however, comes at a great computational cost. To mitigate the issue of high computation times, surrogates, such as

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5

Charles, Giovanni, Timothy M. Wolock, Peter Winskill, Azra Ghani, Samir Bhatt, and Seth Flaxman. "Seq2Seq Surrogates of Epidemic Models to Facilitate Bayesian Inference." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 12 (2023): 14170–77. http://dx.doi.org/10.1609/aaai.v37i12.26658.

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Epidemic models are powerful tools in understanding infectious disease. However, as they increase in size and complexity, they can quickly become computationally intractable. Recent progress in modelling methodology has shown that surrogate models can be used to emulate complex epidemic models with a high-dimensional parameter space. We show that deep sequence-to-sequence (seq2seq) models can serve as accurate surrogates for complex epidemic models with sequence based model parameters, effectively replicating seasonal and long-term transmission dynamics. Once trained, our surrogate can predict

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6

Xu, Lin, Hongyu Nie, Xiangyang Cheng, Qi Wei, Hongyu Chen, and Jianfeng Tao. "Surrogate Model of Hydraulic Actuator for Active Motion Compensation Hydraulic Crane." Electronics 14, no. 13 (2025): 2678. https://doi.org/10.3390/electronics14132678.

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Offshore cranes equipped with active motion compensation (AMC) systems play a vital role in marine engineering tasks such as offshore wind turbine maintenance, subsea operations, and dynamic load positioning under wave-induced disturbances. These systems rely on complex hydraulic actuators whose strongly nonlinear dynamics—often described by differential-algebraic equations (DAEs)—impose significant computational burdens, particularly in real-time applications like hardware-in-the-loop (HIL) simulation, digital twins, and model predictive control. To address this bottleneck, we propose a neura

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7

Chen, Menghui, Xiaoshu Gao, Cheng Chen, Tong Guo, and Weijie Xu. "A Comparative Study of Meta-Modeling for Response Estimation of Stochastic Nonlinear MDOF Systems Using MIMO-NARX Models." Applied Sciences 12, no. 22 (2022): 11553. http://dx.doi.org/10.3390/app122211553.

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Complex dynamic behavior of nonlinear structures makes it challenging for uncertainty analysis through Monte Carlo simulations (MCS). Surrogate modeling presents an efficient and accurate computational alternative for a large number of MCS. The previous study has demonstrated that the multi-input multi-output nonlinear autoregressive with exogenous input (MIMO-NARX) model provides good discrete-time representations of deterministic nonlinear multi-degree-of-freedom (MDOF) structural dynamic systems. Model order reduction (MOR) is executed to eliminate insignificant modes to reduce the computat

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8

Liu, Shizhong, Ziyao Wang, Jingwen Chen, Rui Xu, and Dong Ming. "The Estimation of Knee Medial Force with Substitution Parameters during Walking and Turning." Sensors 24, no. 17 (2024): 5595. http://dx.doi.org/10.3390/s24175595.

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Purpose: Knee adduction, flexion moment, and adduction angle are often used as surrogate parameters of knee medial force. To verify whether these parameters are suitable as surrogates under different walking states, we investigated the correlation between knee medial loading with the surrogates during walking and turning. Methods: Sixteen healthy subjects were recruited to complete straight walk (SW), step turn (ST), and crossover turn (CT). Knee joint moments were obtained using inverse dynamics, and knee medial force was computed using a previously validated musculoskeletal model, Freebody.

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9

Gong, Xu, Zhengqi Gu, and Zhenlei Li. "Surrogate model for aerodynamic shape optimization of a tractor-trailer in crosswinds." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 226, no. 10 (2012): 1325–39. http://dx.doi.org/10.1177/0954407012442295.

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A surrogate model-based aerodynamic shape optimization method applied to the wind deflector of a tractor-trailer is presented in this paper. The aerodynamic drag coefficient of the tractor-trailer with and without the wind deflector subjected to crosswinds is analyzed. The numerical results show that the wind deflector can decrease drag coefficient. Four parameters are used to describe the wind deflector geometry: width, length, height, and angle. A 30-level design of experiments study using the optimal Latin hypercube method was conducted to analyze the sensitivity of the design variables and

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10

Feng, Yang, Chunfa Zhao, Xin Liang, and Zhan Bai. "SNN-Based Surrogate Modeling of Electromagnetic Force and Its Application in Maglev Vehicle Dynamics Simulation." Actuators 14, no. 3 (2025): 112. https://doi.org/10.3390/act14030112.

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The majority of electromagnetic force calculation models employed in maglev vehicle system dynamics focus exclusively on vertical and lateral movement while neglecting the nonlinear magnetization properties of ferromagnetic materials. This oversight leads to discrepancies between the dynamics simulations and actual conditions. To enhance the accuracy of dynamics simulations and evaluate the performance of maglev vehicle systems under various operational conditions, it is imperative to identify an electromagnetic force calculation model that combines accuracy and applicability. To address this

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11

She, N., and D. Basketfield. "Streamflow dynamics at the Puget Sound, Washington: application of a surrogate data method." Nonlinear Processes in Geophysics 12, no. 4 (2005): 461–69. http://dx.doi.org/10.5194/npg-12-461-2005.

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Abstract. Recent progress in nonlinear dynamic theory has inspired hydrologists to apply innovative nonlinear time series techniques to the analysis of streamflow data. However, regardless of the method employed to analyze streamflow data, the first step should be the identification of underlying dynamics using one or more methods that could distinguish between linear and nonlinear, deterministic and stochastic processes from data itself. In recent years a statistically rigorous framework to test whether or not the examined time series is generated by a Gaussian (linear) process undergoing a p

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12

Glaz, Bryan, Li Liu, Peretz P. Friedmann, Jeremy Bain, and Lakshmi N. Sankar. "A Surrogate-Based Approach to Reduced-Order Dynamic Stall Modeling." Journal of the American Helicopter Society 57, no. 2 (2012): 1–9. http://dx.doi.org/10.4050/jahs.57.022002.

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The surrogate-based recurrence framework (SBRF) approach to reduced-order dynamic stall modeling associated with pitching/plunging airfoils subject to fixed or time-varying freestream Mach numbers is described. The SBRF is shown to effectively mimic full-order two-dimensional computational fluid dynamics solutions for unsteady lift, moment, and drag, but at a fraction of the computational cost. In addition to accounting for realistic helicopter rotor blade dynamics, it is shown that the SBRF can model advancing rotor shock induced separation as well as retreating blade stall associated with ex

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13

Koutsoupakis, J., and D. Giagopoulos. "AI-Based Surrogate Models for Multibody Dynamics Systems." Journal of Physics: Conference Series 2647, no. 2 (2024): 022002. http://dx.doi.org/10.1088/1742-6596/2647/2/022002.

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Abstract Advancements in computer sciences and technology allow for implementation of detailed numerical models of a system such as the Finite Element (FE) or Multibody Dynamics (MBD) models. Complex mechanical systems can easily be modelled in detail, yielding accurate results. This opportunity provided by these high-fidelity numerical models has led to the broad application of such methods in development and prototyping of mechanical systems, their optimization and fault analysis and so on. The capability of detailed modelling however usually comes at a great computational cost, with the sim

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14

MAKINO, Kohei, Makoto MIWA, Kohei SHINTANI, Atsuji ABE, and Yutaka SASAKI. "Surrogate modeling of vehicle dynamics using deep learning." Proceedings of Design & Systems Conference 2019.29 (2019): 2209. http://dx.doi.org/10.1299/jsmedsd.2019.29.2209.

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15

Marin-Lopez, A., J. A. Martínez-Cadena, F. Martinez-Martinez, and J. Alvarez-Ramirez. "Surrogate multivariate Hurst exponent analysis of gait dynamics." Chaos, Solitons & Fractals 172 (July 2023): 113605. http://dx.doi.org/10.1016/j.chaos.2023.113605.

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16

Serafino, Aldo, Benoit Obert, and Paola Cinnella. "Multi-Fidelity Gradient-Based Strategy for Robust Optimization in Computational Fluid Dynamics." Algorithms 13, no. 10 (2020): 248. http://dx.doi.org/10.3390/a13100248.

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Efficient Robust Design Optimization (RDO) strategies coupling a parsimonious uncertainty quantification (UQ) method with a surrogate-based multi-objective genetic algorithm (SMOGA) are investigated for a test problem in computational fluid dynamics (CFD), namely the inverse robust design of an expansion nozzle. The low-order statistics (mean and variance) of the stochastic cost function are computed through either a gradient-enhanced kriging (GEK) surrogate or through the less expensive, lower fidelity, first-order method of moments (MoM). Both the continuous (non-intrusive) and discrete (int

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17

Filip, Elena. "Navigating Surrogacy Contracts: Legal Aspects and Key Elements." Interdisciplinary Journal of Research and Development 12, no. 1 (2025): 138. https://doi.org/10.56345/ijrdv12n118.

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This paper examines the surrogacy contract as a fundamental element that ensures the integrity of the surrogacy process while minimizing risks of exploitation and mistreatment of surrogate mothers and newborns. The article analyzes the evolving legal perspectives and debates surrounding surrogacy contracts over the years, focusing on the conditions and key elements essential for their legality and enforcement. As a legally binding agreement between the surrogate mother and the intended parents, the surrogacy contract requires a clear definition of the rights and obligations of each party, with

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18

Blubaugh, Frank. "Surrogate modeling in structural vibration problems with dynamic mode decomposition." Journal of the Acoustical Society of America 152, no. 4 (2022): A134. http://dx.doi.org/10.1121/10.0015794.

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Solving large-scale vibration problems presents a difficult challenge for complicated geometries that is only addressable through the use of large-scale compute resources. These problems not only require extensive compute time and cost, but are also expensive in engineering hours for modeling and analysis. While optimization techniques can help limit engineering time in the loop, the computational requirements of these models have made applying traditional optimization techniques to this class of problem untenable. Dynamic Mode Decomposition (DMD) is an approach that can help bridge this gap b

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19

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

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

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20

Qin, W. J., and J. Q. He. "Optimum Design of Local Cam Profile of a Valve Train." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 224, no. 11 (2010): 2487–92. http://dx.doi.org/10.1243/09544062jmes2116.

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In this paper, optimization of the local cam profile of a valve train modelled by a parameterized Bezier curve is described. Dynamic responses of the valve train are simulated through its multi-body system dynamics model built using ADAMS software. The kriging method is used to build the surrogate model, which presents the relationship between dynamic responses resulting from the multi-body system dynamics simulation and the parameters of the local Bezier profile. The local cam profile is optimized through a generic algorithm, such that the acceleration peak at the valve open phase is reduced

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21

MAKINO, Kohei, Makoto MIWA, Kohei SHINTANI, Atsuji ABE, and Yutaka SASAKI. "Surrogate modeling of vehicle dynamics using Recurrent Neural Networks." Transactions of the JSME (in Japanese) 86, no. 891 (2020): 20–00177. http://dx.doi.org/10.1299/transjsme.20-00177.

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22

Habecker, F., R. Röhse, and T. Klüner. "Dissipative quantum dynamics using the stochastic surrogate Hamiltonian approach." Journal of Chemical Physics 151, no. 13 (2019): 134113. http://dx.doi.org/10.1063/1.5119195.

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23

Tokuda, Isao, Takaya Miyano, and Kazuyuki Aihara. "Surrogate analysis for detecting nonlinear dynamics in normal vowels." Journal of the Acoustical Society of America 110, no. 6 (2001): 3207–17. http://dx.doi.org/10.1121/1.1413749.

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24

Tan, Tian, Jin-song Dai, Yong-tao Zhang, Chao Meng, and Sheng-ye Lin. "Research on the dynamic characterization of hydraulic buffer based on gene expression programming approach." Journal of Physics: Conference Series 2891, no. 9 (2024): 092026. https://doi.org/10.1088/1742-6596/2891/9/092026.

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Abstract This paper is devoted to the dynamic characterization of the autocannon buffer through the surrogate model technique. Autocannon is usually equipped with spring hydraulic buffers, and flow field analysis based on numerical simulation is the focus of buffer characteristic research. However, commercial software is generally computationally expensive and challenging to interact with system dynamic models. To end these, this paper proposes constructing a surrogate model through the gene expression programming (GEP) approach and embedding it into the autocannon bond space model for recoil

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25

Caron, Davide, Ángel Canal-Alonso, and Gabriella Panuccio. "Mimicking CA3 Temporal Dynamics Controls Limbic Ictogenesis." Biology 11, no. 3 (2022): 371. http://dx.doi.org/10.3390/biology11030371.

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Mesial temporal lobe epilepsy (MTLE) is the most common partial complex epilepsy in adults and the most unresponsive to medications. Electrical deep brain stimulation (DBS) of the hippocampus has proved effective in controlling seizures in epileptic rodents and in drug-refractory MTLE patients. However, current DBS paradigms implement arbitrary fixed-frequency or patterned stimuli, disregarding the temporal profile of brain electrical activity. The latter, herein included hippocampal spontaneous firing, has been shown to follow lognormal temporal dynamics. Here, we present a novel paradigm to

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26

Zeng, Wei, Xian Chao Wang, and Ying Sheng Wang. "Surrogating for High Dimensional Computationally Expensive Multi-Modal Functions with Elliptical Basis Function Models." Applied Mechanics and Materials 733 (February 2015): 880–84. http://dx.doi.org/10.4028/www.scientific.net/amm.733.880.

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In the engineering design process, approximation Technique could guarantee the fitting precision, speed up the design process and reduce design costs. To a certain extent, surrogate models could replace time-consuming and highly accurate computational fluid dynamics analysis gradually. In this paper, we take Optimal Latin Hypercube Sampling experimental design strategies to determine the sample space and error analysis test sample, adopt the principle of infilling criteria based on the maximum error to improve the accuracy of the surrogate model, test the unimodal and multimodal expensive func

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27

Mariani, Valerio, Leonardo Pulga, Gian Marco Bianchi, Stefania Falfari, and Claudio Forte. "Machine Learning-Based Identification Strategy of Fuel Surrogates for the CFD Simulation of Stratified Operations in Low Temperature Combustion Modes." Energies 14, no. 15 (2021): 4623. http://dx.doi.org/10.3390/en14154623.

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Many researchers in industry and academia are showing an increasing interest in the definition of fuel surrogates for Computational Fluid Dynamics simulation applications. This need is mainly driven by the necessity of the engine research community to anticipate the effects of new gasoline formulations and combustion modes (e.g., Homogeneous Charge Compression Ignition, Spark Assisted Compression Ignition) to meet future emission regulations. Since those solutions strongly rely on the tailored mixture distribution, the simulation and accurate prediction of the mixture formation will be mandato

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28

Preen, Richard J., and Larry Bull. "Design Mining Interacting Wind Turbines." Evolutionary Computation 24, no. 1 (2016): 89–111. http://dx.doi.org/10.1162/evco_a_00144.

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An initial study has recently been presented of surrogate-assisted evolutionary algorithms used to design vertical-axis wind turbines wherein candidate prototypes are evaluated under fan-generated wind conditions after being physically instantiated by a 3D printer. Unlike other approaches, such as computational fluid dynamics simulations, no mathematical formulations were used and no model assumptions were made. This paper extends that work by exploring alternative surrogate modelling and evolutionary techniques. The accuracy of various modelling algorithms used to estimate the fitness of eval

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29

Ueki, Ryosuke, Shota Hayashi, Masaya Tsunoda, et al. "Nongenetic control of receptor signaling dynamics using a DNA-based optochemical tool." Chemical Communications 57, no. 48 (2021): 5969–72. http://dx.doi.org/10.1039/d1cc01968f.

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30

Thiel, M., M. C. Romano, J. Kurths, M. Rolfs, and R. Kliegl. "Generating surrogates from recurrences." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 366, no. 1865 (2007): 545–57. http://dx.doi.org/10.1098/rsta.2007.2109.

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In this paper, we present an approach to recover the dynamics from recurrences of a system and then generate (multivariate) twin surrogate (TS) trajectories. In contrast to other approaches, such as the linear-like surrogates, this technique produces surrogates which correspond to an independent copy of the underlying system, i.e. they induce a trajectory of the underlying system visiting the attractor in a different way. We show that these surrogates are well suited to test for complex synchronization, which makes it possible to systematically assess the reliability of synchronization analyse

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31

Small, Michael, and Kevin Judd. "Detecting Nonlinearity in Experimental Data." International Journal of Bifurcation and Chaos 08, no. 06 (1998): 1231–44. http://dx.doi.org/10.1142/s0218127498000966.

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The technique of surrogate data has been used as a method to test for membership of particular classes of linear systems. We suggest an obvious extension of this to classes of nonlinear parametric models and demonstrate our methods with respiratory data from sleeping human infants. Although our data are clearly distinct from the different classes of linear systems we are unable to distinguish between our data and surrogates generated by nonlinear models. Hence we conclude that human respiration is likely to be a nonlinear system with more than two degrees of freedom with a limit cycle that is

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32

Wang, Xu, and Kai Liu. "A Crash Surrogate Metric considering Traffic Flow Dynamics in a Motorway Corridor." Journal of Advanced Transportation 2018 (June 27, 2018): 1–7. http://dx.doi.org/10.1155/2018/9349418.

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We proposed a new crash surrogate metric, i.e., the maximum disturbance that a car following scenario can accommodate, to represent potential crash risks with a simple closed form. The metric is developed in consideration of traffic flow dynamics. Then, we compared its performance in predicting the rear-end crash risks for motorway on-ramps with other two surrogate measures (time to collision and aggregated crash index). To this end, a one-lane on-ramp of Pacific Motorway, Australia, was selected for this case study. Due to the lack of crash data on the study site, historical crash counts were

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33

Bender, Niels C., Torben Ole Andersen, and Henrik C. Pedersen. "Feasibility of Deep Neural Network Surrogate Models in Fluid Dynamics." Modeling, Identification and Control: A Norwegian Research Bulletin 40, no. 2 (2019): 71–87. http://dx.doi.org/10.4173/mic.2019.2.1.

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34

Mooney, Barbara L., Brian H. Morrow, Keith Van Nostrand, et al. "Elucidating the Properties of Surrogate Fuel Mixtures Using Molecular Dynamics." Energy & Fuels 30, no. 2 (2016): 784–95. http://dx.doi.org/10.1021/acs.energyfuels.5b01468.

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35

Gan, Chunbiao, and Shimin He. "Surrogate test for noise-contaminated dynamics in the Duffing oscillator." Chaos, Solitons & Fractals 38, no. 5 (2008): 1517–22. http://dx.doi.org/10.1016/j.chaos.2007.01.134.

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36

Forrester, Alexander I. J., Neil W. Bressloff, and Andy J. Keane. "Optimization using surrogate models and partially converged computational fluid dynamics simulations." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 462, no. 2071 (2006): 2177–204. http://dx.doi.org/10.1098/rspa.2006.1679.

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Efficient methods for global aerodynamic optimization using computational fluid dynamics simulations should aim to reduce both the time taken to evaluate design concepts and the number of evaluations needed for optimization. This paper investigates methods for improving such efficiency through the use of partially converged computational fluid dynamics results. These allow surrogate models to be built in a fraction of the time required for models based on converged results. The proposed optimization methodologies increase the speed of convergence to a global optimum while the computer resource

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37

Fouladinejad, Nariman, Nima Fouladinejad, Mohamad Kasim Abdul Jalil, and Jamaludin Mohd Taib. "Development of a surrogate-based vehicle dynamic model to reduce computational delays in a driving simulator." SIMULATION 92, no. 12 (2016): 1087–102. http://dx.doi.org/10.1177/0037549716675956.

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The development of a real-time driving simulator involves highly complex integrated and interdependent subsystems that require a large amount of computational time. When advanced hardware is unavailable for economic reasons, achieving real-time simulation is challenging, and thus delays are inevitable. Moreover, computational delays in the response of driving simulator subsystems reduce the fidelity of the simulation. In this paper, we propose a technique to decrease computational delays in a driving simulator. We used approximation techniques, sensitivity analysis, decomposition, and sampling

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38

Rubin, Sergio, and Michel Crucifix. "Earth’s Complexity Is Non-Computable: The Limits of Scaling Laws, Nonlinearity and Chaos." Entropy 23, no. 7 (2021): 915. http://dx.doi.org/10.3390/e23070915.

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Current physics commonly qualifies the Earth system as ‘complex’ because it includes numerous different processes operating over a large range of spatial scales, often modelled as exhibiting non-linear chaotic response dynamics and power scaling laws. This characterization is based on the fundamental assumption that the Earth’s complexity could, in principle, be modeled by (surrogated by) a numerical algorithm if enough computing power were granted. Yet, similar numerical algorithms also surrogate different systems having the same processes and dynamics, such as Mars or Jupiter, although being

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39

Lu, Qiuyu, Yuqi Cao, Pingping Xie, Ying Chen, and Yingming Lin. "A Scalable Data-Driven Surrogate Model for 3D Dynamic Wind Farm Wake Prediction Using Physics-Inspired Neural Networks and Wind Box Decomposition." Energies 18, no. 13 (2025): 3356. https://doi.org/10.3390/en18133356.

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Wake effects significantly reduce efficiency and increase structural loads in wind farms. Therefore, accurate and computationally efficient models are crucial for wind farm layout optimization and operational control. High-fidelity computational fluid dynamics (CFD) simulations, while accurate, are too slow for these tasks, whereas faster analytical models often lack dynamic fidelity and 3D detail, particularly under complex conditions. Existing data-driven surrogate models based on neural networks often struggle with the high dimensionality of the flow field and scalability to large wind farm

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40

Hulsman, Paul, Søren Juhl Andersen, and Tuhfe Göçmen. "Optimizing wind farm control through wake steering using surrogate models based on high-fidelity simulations." Wind Energy Science 5, no. 1 (2020): 309–29. http://dx.doi.org/10.5194/wes-5-309-2020.

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Abstract. This paper aims to develop fast and reliable surrogate models for yaw-based wind farm control. The surrogates, based on polynomial chaos expansion (PCE), are built using high-fidelity flow simulations coupled with aeroelastic simulations of the turbine performance and loads. Developing a model for wind farm control is a challenging control problem due to the time-varying dynamics of the wake. The wind farm control strategy is optimized for both the power output and the loading of the turbines. The optimization performed using two Vestas V27 turbines in a row for a specific atmospheri

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41

Wenink, Robert, Martin van der Eijk, Neil Yorke-Smith, and Peter Wellens. "Multi-fidelity Kriging extrapolation together with CFD for the design of the cross-section of a falling lifeboat." International Shipbuilding Progress 70, no. 2 (2023): 115–50. http://dx.doi.org/10.3233/isp-230013.

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Surrogate modelling techniques such as Kriging are a popular means for cheaply emulating the response of expensive Computational Fluid Dynamics (CFD) simulations. These surrogate models are often used for exploring a parameterised design space and identifying optimal designs. Multi-fidelity Kriging extends the methodology to incorporate data of variable accuracy and costs to create a more effective surrogate. This work recognises that the grid convergence property of CFD solvers is currently an unused source of information and presents a novel method that, by leveraging the data structure impl

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Daniel Marjavaara, B., T. Staffan Lundström, Tushar Goel, Yolanda Mack, and Wei Shyy. "Hydraulic Turbine Diffuser Shape Optimization by Multiple Surrogate Model Approximations of Pareto Fronts." Journal of Fluids Engineering 129, no. 9 (2007): 1228–40. http://dx.doi.org/10.1115/1.2754324.

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A multiple surrogate-based optimization strategy in conjunction with an evolutionary algorithm has been employed to optimize the shape of a simplified hydraulic turbine diffuser utilizing three-dimensional Reynolds-averaged Navier–Stokes computational fluid dynamics solutions. Specifically, the diffuser performance is optimized by changing five geometric design variables to maximize the average pressure recovery factor for two inlet boundary conditions with different swirl, corresponding to different operating modes of the hydraulic turbine. Polynomial response surfaces and radial basis neural

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43

Ma, Xiaopeng, Jinsheng Zhao, Desheng Zhou, Kai Zhang, and Yapeng Tian. "Deep Graph Learning-Based Surrogate Model for Inverse Modeling of Fractured Reservoirs." Mathematics 12, no. 5 (2024): 754. http://dx.doi.org/10.3390/math12050754.

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Inverse modeling can estimate uncertain parameters in subsurface reservoirs and provide reliable numerical models for reservoir development and management. The traditional simulation-based inversion method usually requires numerous numerical simulations, which is time-consuming. Recently, deep learning-based surrogate models have been widely studied as an alternative to numerical simulation, which can significantly improve the solving efficiency of inversion. However, for reservoirs with complex fracture distribution, constructing the surrogate model of numerical simulation presents a signific

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44

Laisa, Cristina Juffo Campos, Betencurte da Silva Wellington, Carolina Spindola Rangel Dias Ana, and Cesar Sampaio Dutra Julio. "Exploring Digital Twins of Nonlinear Systems through Meta-Modeling with Echo State Networks." Latin-American Journal of Computing 11, no. 2 (2024): 13–22. https://doi.org/10.5281/zenodo.12169048.

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Effective process monitoring, and control rely on precise dynamic models<strong> </strong>can capture the inherent nonlinearities of chemical systems. However, rigorous modeling of complex industrial processes can be computationally demanding. Meta modeling using machine learning methodologies offers a viable approach to generate computationally efficient surrogate representations. Specifically, Echo State Networks (ESNs) are a promising neural network approach for meta-modeling nonlinear dynamical systems. ESNs simplify training through fixed input weights while they focus learning on output

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45

Nikolaou, Eleftherios, Spyridon Kilimtzidis, and Vassilis Kostopoulos. "Winglet Design for Aerodynamic and Performance Optimization of UAVs via Surrogate Modeling." Aerospace 12, no. 1 (2025): 36. https://doi.org/10.3390/aerospace12010036.

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The aerodynamic performance of an aircraft can be significantly enhanced by incorporating wingtip devices, such as winglets, which primarily reduce lift-induced drag caused by wingtip vortices. This study introduces a comprehensive optimization framework for designing winglets on a Class I fixed-wing mini-UAV, aiming to maximize aerodynamic efficiency and operational performance. Using surrogate-based optimization (SBO) techniques, this research developed winglet geometries with varying geometric parameters such as length, cant angle, and sweep angle with their performance being evaluated thro

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46

Hue, Keat Yung, Jin Hau Lew, Maung Maung Myo Thant, Omar K. Matar, Paul F. Luckham, and Erich A. Müller. "Molecular Dynamics Simulation of Polyacrylamide Adsorption on Calcite." Molecules 28, no. 17 (2023): 6367. http://dx.doi.org/10.3390/molecules28176367.

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In poorly consolidated carbonate rock reservoirs, solids production risk, which can lead to increased environmental waste, can be mitigated by injecting formation-strengthening chemicals. Classical atomistic molecular dynamics (MD) simulation is employed to model the interaction of polyacrylamide-based polymer additives with a calcite structure, which is the main component of carbonate formations. Amongst the possible calcite crystal planes employed as surrogates of reservoir rocks, the (1 0 4) plane is shown to be the most suitable surrogate for assessing the interactions with chemicals due t

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47

Hakkak Moghadam Torbati, Armin, Christian Georgiev, Daria Digileva, et al. "Nonlinear Dynamics of MEG and EMG: Stability and Similarity Analysis." Brain Sciences 15, no. 7 (2025): 681. https://doi.org/10.3390/brainsci15070681.

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Background: Sensorimotor beta oscillations are critical for motor control and become synchronized with muscle activity during sustained contractions, forming corticomuscular coherence (CMC). Although beta activity manifests in transient bursts, suggesting nonlinear behavior, most studies rely on linear analyses, leaving the underlying dynamic structure of brain–muscle interactions underexplored. Objectives: To investigate the nonlinear dynamics underlying beta oscillations during isometric contraction. Methods: MEG and EMG were recorded from 17 right-handed healthy adults performing a 10 min i

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48

Tuan, Nguyen Hung, Le Xuan Huynh, and Pham Hoang Anh. "A fuzzy finite element algorithm based on response surface method for free vibration analysis of structure." Vietnam Journal of Mechanics 37, no. 1 (2015): 17–27. http://dx.doi.org/10.15625/0866-7136/37/1/3923.

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This paper introduces an improved response surface-based fuzzy finite element analysis of structural dynamics. The free vibration of structure is established using superposition method, so that fuzzy displacement responses can be presented as functions of fuzzy mode shapes and fuzzy natural frequencies. Instead of direct determination of these fuzzy quantities by modal analysis which will involve the calculation of the whole finite element model, the paper proposes a felicitous approach to design the response surface as surrogate model for the problem. In the design of the surrogate model, com

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49

Fu, Chao, Zhaoli Zheng, Weidong Zhu, Zhongliang Xie, Weiyang Qin, and Kuan Lu. "Nonlinear dynamics of discontinuous uncertain oscillators with unilateral constraints." Chaos: An Interdisciplinary Journal of Nonlinear Science 32, no. 12 (2022): 123112. http://dx.doi.org/10.1063/5.0125365.

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Nonlinear dynamics of discontinuous oscillators with unilateral constraints and non-random parametric uncertainties are investigated. Nonlinear oscillators considering single- and double-sided constraints are carefully constructed to exhibit rich bifurcations, such as period-doubling and Neimark–Sacker bifurcations. In deterministic amplitude–frequency responses, both hardening and softening effects are induced by non-smooth contact-type nonlinearities. Stabilities of the solutions are determined by the shooting method and the monodromy matrix. To effectively quantify the behaviors of nonlinea

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Hirata, Yoshito, Masanori Shiro, and José M. Amigó. "Surrogate Data Preserving All the Properties of Ordinal Patterns up to a Certain Length." Entropy 21, no. 7 (2019): 713. http://dx.doi.org/10.3390/e21070713.

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We propose a method for generating surrogate data that preserves all the properties of ordinal patterns up to a certain length, such as the numbers of allowed/forbidden ordinal patterns and transition likelihoods from ordinal patterns into others. The null hypothesis is that the details of the underlying dynamics do not matter beyond the refinements of ordinal patterns finer than a predefined length. The proposed surrogate data help construct a test of determinism that is free from the common linearity assumption for a null-hypothesis.

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