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Статті в журналах з теми "Data-driven model order reduction"
Nagy, Peter, and Marco Fossati. "Adaptive Data-Driven Model Order Reduction for Unsteady Aerodynamics." Fluids 7, no. 4 (April 6, 2022): 130. http://dx.doi.org/10.3390/fluids7040130.
Повний текст джерелаGosea, Ion Victor, and Athanasios C. Antoulas. "Data-driven model order reduction of quadratic-bilinear systems." Numerical Linear Algebra with Applications 25, no. 6 (July 22, 2018): e2200. http://dx.doi.org/10.1002/nla.2200.
Повний текст джерелаShah, Aarohi, and Julian J. Rimoli. "Smart parts: Data-driven model order reduction for nonlinear mechanical assemblies." Finite Elements in Analysis and Design 200 (March 2022): 103682. http://dx.doi.org/10.1016/j.finel.2021.103682.
Повний текст джерелаSarna, Neeraj, and Peter Benner. "Data-Driven model order reduction for problems with parameter-dependent jump-discontinuities." Computer Methods in Applied Mechanics and Engineering 387 (December 2021): 114168. http://dx.doi.org/10.1016/j.cma.2021.114168.
Повний текст джерелаPierquin, A., T. Henneron, and S. Clenet. "Data-Driven Model-Order Reduction for Magnetostatic Problem Coupled With Circuit Equations." IEEE Transactions on Magnetics 54, no. 3 (March 2018): 1–4. http://dx.doi.org/10.1109/tmag.2017.2771358.
Повний текст джерелаPeng, Haijun, Ningning Song, and Ziyun Kan. "Data-driven model order reduction with proper symplectic decomposition for flexible multibody system." Nonlinear Dynamics 107, no. 1 (November 6, 2021): 173–203. http://dx.doi.org/10.1007/s11071-021-06990-3.
Повний текст джерелаKim, Hyejin, Haeseong Cho, Sihun Lee, SangJoon Shin, and Haedeong Kim. "Development of an Efficient Nonlinear Structural Analysis Using Data-driven Model Order Reduction." Transactions of the Korean Society for Noise and Vibration Engineering 31, no. 6 (December 20, 2021): 604–13. http://dx.doi.org/10.5050/ksnve.2021.31.6.604.
Повний текст джерелаGosea, I. V., M. Petreczky, and A. C. Antoulas. "Data-Driven Model Order Reduction of Linear Switched Systems in the Loewner Framework." SIAM Journal on Scientific Computing 40, no. 2 (January 2018): B572—B610. http://dx.doi.org/10.1137/17m1120233.
Повний текст джерелаSpinosa, Angelo Giuseppe, Arturo Buscarino, Luigi Fortuna, Matteo Iafrati, and Giuseppe Mazzitelli. "Data-driven order reduction in Hammerstein–Wiener models of plasma dynamics." Engineering Applications of Artificial Intelligence 100 (April 2021): 104180. http://dx.doi.org/10.1016/j.engappai.2021.104180.
Повний текст джерелаCasciati, Fabio, and Lucia Faravelli. "Sensor placement driven by a model order reduction (MOR) reasoning." Smart Structures and Systems 13, no. 3 (March 25, 2014): 343–52. http://dx.doi.org/10.12989/sss.2014.13.3.343.
Повний текст джерелаДисертації з теми "Data-driven model order reduction"
Quaranta, Giacomo. "Efficient simulation tools for real-time monitoring and control using model order reduction and data-driven techniques." Doctoral thesis, Universitat Politècnica de Catalunya, 2019. http://hdl.handle.net/10803/667474.
Повний текст джерелаLa simulación numérica, el uso de ordenadores para ejecutar un programa que implementa un modelo matemático de un sistema físico, es una parte importante del mundo tecnológico actual. En muchos campos de la ciencia y la ingeniería es necesario estudiar el comportamiento de sistemas cuyos modelos matemáticos son demasiado complejos para proporcionar soluciones analíticas, haciendo posible la evaluación virtual de las respuestas de los sistemas (gemelos virtuales). Esto reduce drásticamente el número de pruebas experimentales para los diseños precisos del sistema real que el modelo numérico representa. Sin embargo, estos gemelos virtuales, basados en métodos clásicos que hacen uso de una rica representación del sistema (por ejemplo, el método de elementos finitos), rara vez permiten la retroalimentación en tiempo real, incluso cuando se considera la computación en plataformas de alto rendimiento. En estas circunstancias, el rendimiento en tiempo real requerido en algunas aplicaciones se ve comprometido. En efecto, los gemelos virtuales son estáticos, es decir, se utilizan en el diseño de sistemas complejos y sus componentes, pero no se espera que acomoden o asimilen los datos para definir sistemas de aplicación dinámicos basados en datos. Además, se suelen apreciar desviaciones significativas entre la respuesta observada y la predicha por el modelo, debido a inexactitudes en los modelos empleados, en la determinación de los parámetros del modelo o en su evolución temporal. En esta tesis se proponen diferentes métodos para resolver estas limitaciones con el fin de realizar un seguimiento y un control en tiempo real. En la primera parte se utilizan técnicas de Reducción de Modelos para satisfacer las restricciones en tiempo real; estas técnicas calculan una buena aproximación de la solución simplificando el procedimiento de resolución en lugar del modelo. La precisión de la solución no se ve comprometida y se pueden realizar simulaciones efficientes (gemelos digitales). En la segunda parte se emplea la modelización basada en datos para llenar el vacío entre la solución paramétrica, calculada utilizando técnicas de reducción de modelos no intrusivas, y los campos medidos, con el fin de hacer posibles los sistemas de aplicación dinámicos basados en datos (gemelos híbridos).
La simulation numérique, c'est-à-dire l'utilisation des ordinateurs pour exécuter un programme qui met en oeuvre un modèle mathématique d'un système physique, est une partie importante du monde technologique actuel. Elle est nécessaire dans de nombreux domaines scientifiques et techniques pour étudier le comportement de systèmes dont les modèles mathématiques sont trop complexes pour fournir des solutions analytiques et elle rend possible l'évaluation virtuelle des réponses des systèmes (jumeaux virtuels). Cela réduit considérablement le nombre de tests expérimentaux nécessaires à la conception précise du système réel que le modèle numérique représente. Cependant, ces jumeaux virtuels, basés sur des méthodes classiques qui utilisent une représentation fine du système (ex. méthode des éléments finis), permettent rarement une rétroaction en temps réel, même dans un contexte de calcul haute performance, fonctionnant sur des plates-formes puissantes. Dans ces circonstances, les performances en temps réel requises dans certaines applications sont compromises. En effet, les jumeaux virtuels sont statiques, c'est-à-dire qu'ils sont utilisés dans la conception de systèmes complexes et de leurs composants, mais on ne s'attend pas à ce qu'ils prennent en compte ou assimilent des données afin de définir des systèmes d'application dynamiques pilotés par les données. De plus, des écarts significatifs entre la réponse observée et celle prévue par le modèle sont généralement constatés en raison de l'imprécision des modèles employés, de la détermination des paramètres du modèle ou de leur évolution dans le temps. Dans cette thèse, nous proposons di érentes méthodes pour résoudre ces handicaps afin d'effectuer une surveillance et un contrôle en temps réel. Dans la première partie, les techniques de Réduction de Modèles sont utilisées pour tenir compte des contraintes en temps réel ; elles calculent une bonne approximation de la solution en simplifiant la procédure de résolution plutôt que le modèle. La précision de la solution n'est pas compromise et des simulations e caces peuvent être réalisées (jumeaux numériquex). Dans la deuxième partie, la modélisation pilotée par les données est utilisée pour combler l'écart entre la solution paramétrique calculée, en utilisant des techniques de réduction de modèles non intrusives, et les champs mesurés, afin de rendre possibles des systèmes d'application dynamiques basés sur les données (jumeaux hybrides).
Ibañez, Pinillo Ruben. "Advanced physics-based and data-driven strategies." Thesis, Ecole centrale de Nantes, 2019. http://www.theses.fr/2019ECDN0021.
Повний текст джерелаSimulation Based Engineering Science (SBES) has brought major improvements in optimization, control and inverse analysis, all leading to a deeper understanding in many processes occurring in the real world. These noticeable breakthroughs are present in a vast variety of sectors such as aeronautic or automotive industries, mobile telecommunications or healthcare among many other fields. Nevertheless, SBES is currently confronting several difficulties to provide accurate results in complex industrial problems. Apart from the high computational costs associated with industrial applications, the errors introduced by constitutive modeling become more and more important when dealing with new materials. Concurrently, an unceasingly growing interest in concepts such as Big-Data, Machine Learning or Data-Analytics has been experienced. Indeed, this interest is intrinsically motivated by an exhaustive development in both dataacquisition and data-storage systems. For instance, an aircraft may produce over 500 GB of data during a single flight. This panorama brings a perfect opportunity to the socalled Dynamic Data Driven Application Systems (DDDAS), whose main objective is to merge classical simulation algorithms with data coming from experimental measures in a dynamic way. Within this scenario, data and simulations would no longer be uncoupled but rather a symbiosis that is to be exploited would achieve milestones which were inconceivable until these days. Indeed, data will no longer be understood as a static calibration of a given constitutive model but rather the model will be corrected dynamically as soon as experimental data and simulations tend to diverge. Several numerical algorithms will be presented throughout this manuscript whose main objective is to strengthen the link between data and computational mechanics. The first part of the thesis is mainly focused on parameter identification, data-driven and data completion techniques. The second part is focused on Model Order Reduction (MOR) techniques, since they constitute a fundamental ally to achieve real time constraints arising from DDDAS framework
Waseem, Abdullah. "Numerical Homogenization and Model Reduction for Transient Heat, Diffusion and coupled Mechanics Problems." Thesis, Ecole centrale de Nantes, 2020. http://www.theses.fr/2020ECDN0028.
Повний текст джерелаIn this thesis computationally efficient numerical homogenization techniques are presented for diffusion phenomena in heterogeneous materials. As a preliminary step, a model reduction for the transient heat diffusion equation is performed at the micro-scale using component mode synthesis, which provides an emergent enriched-continuum description at the macro-scale. Based on the location of the enrichmentvariables, either on the finite element nodes or the quadrature points, two spatial discretization schemes are analyzed for the enrichedcontinuum. The proposed model reduction is also extended to the transient mass diffusion coupled to the mechanics with application to lithium-ion batteries. Chemical potential and strain fields formulation is used which allows the use of standard C0-continuous finite elements. The micro-scale problem, which usually involves an expensive solution of the coupled mass diffusionmechanics problem is now replaced by a set of ordinary differential equations through model reduction. Finally, an alternative model reduction approach using data-driven mechanics is explored. It relies on a direct search and interpolation from a database instead of the solution of a microscopic problem. The database is constructed and stored using the microscopic calculations in an offline stage. It also provides a route to extend the proposed model reduction method to the nonlinear regime
Taddei, Tommaso. "Model order reduction methods for data assimilation : state estimation and structural health monitoring." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/108942.
Повний текст джерелаCataloged from PDF version of thesis.
Includes bibliographical references (pages 243-258).
The objective of this thesis is to develop and analyze model order reduction approaches for the efficient integration of parametrized mathematical models and experimental measurements. Model Order Reduction (MOR) techniques for parameterized Partial Differential Equations (PDEs) offer new opportunities for the integration of models and experimental data. First, MOR techniques speed up computations allowing better explorations of the parameter space. Second, MOR provides actionable tools to compress our prior knowledge about the system coming from the parameterized best-knowledge model into low-dimensional and more manageable forms. In this thesis, we demonstrate how to take advantage of MOR to design computational methods for two classes of problems in data assimilation. In the first part of the thesis, we discuss and extend the Parametrized-Background Data-Weak (PBDW) approach for state estimation. PBDW combines a parameterized best knowledge mathematical model and experimental data to rapidly estimate the system state over the domain of interest using a small number of local measurements. The approach relies on projection-by-data, and exploits model reduction techniques to encode the knowledge of the parametrized model into a linear space appropriate for real-time evaluation. In this work, we extend the PBDW formulation in three ways. First, we develop an experimental a posteriori estimator for the error in the state. Second, we develop computational procedures to construct local approximation spaces in subregions of the computational domain in which the best-knowledge model is defined. Third, we present an adaptive strategy to handle experimental noise in the observations. We apply our approach to a companioni heat transfer experiment to prove the effectiveness of our technique. In the second part of the thesis, we present a model-order reduction approach to simulation based classification, with particular application to Structural Health Monitoring (SHM). The approach exploits (i) synthetic results obtained by repeated solution of a parametrized PDE for different values of the parameters, (ii) machine-learning algorithms to generate a classifier that monitors the state of damage of the system, and (iii) a reduced basis method to reduce the computational burden associated with the model evaluations. The approach is based on an offline/online computational decomposition. In the offline stage, the fields associated with many different system configurations, corresponding to different states of damage, are computed and then employed to teach a classifier. Model reduction techniques, ideal for this many-query context, are employed to reduce the computational burden associated with the parameter exploration. In the online stage, the classifier is used to associate measured data to the relevant diagnostic class. In developing our approach for SHM, we focus on two specific aspects. First, we develop a mathematical formulation which properly integrates the parameterized PDE model within the classification problem. Second, we present a sensitivity analysis to take into account the error in the model. We illustrate our method and we demonstrate its effectiveness through the vehicle of a particular companion experiment, a harmonically excited microtruss.
by Tommaso Taddei.
Ph. D.
Lauzeral, Nathan. "Reduced order and sparse representations for patient-specific modeling in computational surgery." Thesis, Ecole centrale de Nantes, 2019. http://www.theses.fr/2019ECDN0062.
Повний текст джерелаThis thesis investigates the use of model order reduction methods based on sparsity-related techniques for the development of real-time biophysical modeling. In particular, it focuses on the embedding of interactive biophysical simulation into patient-specific models of tissues and organs to enhance medical images and assist the clinician in the process of informed decision making. In this context, three fundamental bottlenecks arise. The first lies in the embedding of the shape parametrization into the parametric reduced order model to faithfully represent the patient’s anatomy. A non-intrusive approach relying on a sparse sampling of the space of anatomical features is introduced and validated. Then, we tackle the problem of data completion and image reconstruction from partial or incomplete datasets based on physical priors. The proposed solution has the potential to perform scene registration in the context of augmented reality for laparoscopy. Quasi-real-time computations are reached by using a new hyperreduction approach based on a sparsity promoting technique. Finally, the third challenge concerns the representation of biophysical systems under uncertainty of the underlying parameters. It is shown that traditional model order reduction approaches are not always successful in producing a low dimensional representation of a model, in particular in the case of electrosurgery simulation. An alternative is proposed using a metamodeling approach. To this end, we successfully extend the use of sparse regression methods to the case of systems with stochastic parameters
Akhtar, Sabina. "Vérification Formelle d'Algorithmes Distribués en PlusCal-2." Phd thesis, Université de Lorraine, 2012. http://tel.archives-ouvertes.fr/tel-00815570.
Повний текст джерелаKoc, Birgul. "Numerical Analysis for Data-Driven Reduced Order Model Closures." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/103202.
Повний текст джерелаDoctor of Philosophy
In many realistic applications, obtaining an accurate approximation to a given problem can require a tremendous number of degrees of freedom. Solving these large systems of equations can take days or even weeks on standard computational platforms. Thus, lower-dimensional models, i.e., reduced order models (ROMs), are often used instead. The ROMs are computationally efficient and accurate when the underlying system has dominant and recurrent spatial structures. Our contribution to reduced order modeling is adding a data-driven correction term, which carries important information and yields better ROM approximations. This dissertation's theoretical and numerical results show that the new ROM equipped with a closure term yields more accurate approximations than the standard ROM.
Hammond, Janelle K. "Méthodes des bases réduites pour la modélisation de la qualité de l'air urbaine." Thesis, Paris Est, 2017. http://www.theses.fr/2017PESC1230/document.
Повний текст джерелаThe principal objective of this thesis is the development of low-cost numerical tools for spatial mapping of pollutant concentrations from field observations and advanced deterministic models. With increased pollutant emissions and exposure due to mass urbanization and development worldwide, air quality measurement campaigns and epidemiology studies of the association between air pollution and adverse health effects have become increasingly common. However, as air pollution concentrations are highly variable spatially and temporally, the sensitivity and accuracy of these epidemiology studies is often deteriorated by exposure misclassi cation due to poor estimates of individual exposures. Data assimilation methods incorporate available measurement data and mathematical models to provide improved approximations of the concentration. These methods, when based on an advanced deterministic air quality models (AQMs), could provide spatially-rich small-scale approximations and can enable better estimates of effects and exposures. However, these methods can be computationally expensive. They require repeated solution of the model, which could itself be costly. In this work we investigate a combined reduced basis (RB) data assimilation method for use with advanced AQMs on urban scales. We want to diminish the cost of resolution, using RB arguments, and incorporate measurement data to improve the quality of the solution. We extend the Parameterized-Background Data-Weak (PBDW) method to physically-based AQMs. This method can rapidly estimate "online" pollutant concentrations at urban scale, using available AQMs in a non-intrusive and computationally effcient manner, reducing computation times by factors up to hundreds. We apply this method in case studies representing urban residential pollution of PM2.5, and we study the stability of the method depending on the placement or air quality sensors. Results from the PBDW are compared to the Generalized Empirical Interpolation Method (GEIM) and a standard inverse problem, the adjoint method, in order to measure effciency of the method. This comparison shows possible improvement in precision and great improvement in computation cost with respect to classical methods. We fi nd that the PBDW method shows promise for the real-time reconstruction of a pollution eld in large-scale problems, providing state estimation with approximation error generally under 10% when applied to an imperfect model
Ulin, Samuel. "Digging deep : A data-driven approach to model reduction in a granular bulldozing scenario." Thesis, Umeå universitet, Institutionen för fysik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-152498.
Повний текст джерелаSavas, Berkant. "Algorithms in data mining using matrix and tensor methods." Doctoral thesis, Linköpings universitet, Beräkningsvetenskap, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-11597.
Повний текст джерелаКниги з теми "Data-driven model order reduction"
Kisanga, Elineema, Vincent Leyaro, Wahabi Matengo, Michael Noble, Helen Barnes, and Gemma Wright. Assessing the distributional impact of lowering the value-added tax rate for standard-rated items in Tanzania and options for recouping revenue losses. 38th ed. UNU-WIDER, 2021. http://dx.doi.org/10.35188/unu-wider/2021/976-1.
Повний текст джерелаBacior, Stanisław. Optymalizacja wiejskich układów gruntowych – badania eksperymentalne. Publishing House of the University of Agriculture in Krakow, 2019. http://dx.doi.org/10.15576/978-83-66602-37-3.
Повний текст джерелаQueloz, Matthieu. The Practical Origins of Ideas. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198868705.001.0001.
Повний текст джерелаLi, Quan. Using R for Data Analysis in Social Sciences. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190656218.001.0001.
Повний текст джерелаBaumgaertner, Annette. Mixed Transcortical Aphasia: Repetition without Meaning. Edited by Anastasia M. Raymer and Leslie J. Gonzalez Rothi. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199772391.013.10.
Повний текст джерелаЧастини книг з теми "Data-driven model order reduction"
Pontes Duff, Igor, Pawan Goyal, and Peter Benner. "Data-Driven Identification of Rayleigh-Damped Second-Order Systems." In Realization and Model Reduction of Dynamical Systems, 255–72. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95157-3_14.
Повний текст джерелаWang, Jian, Wei Xing, Robert M. Kirby, and Miaomiao Zhang. "Data-Driven Model Order Reduction for Diffeomorphic Image Registration." In Lecture Notes in Computer Science, 694–705. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20351-1_54.
Повний текст джерелаDeschrijver, Dirk, and Tom Dhaene. "Data-Driven Model Order Reduction Using Orthonormal Vector Fitting." In Mathematics in Industry, 341–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78841-6_16.
Повний текст джерелаGrundel, Sara, Nils Hornung, and Sarah Roggendorf. "Numerical Aspects of Model Order Reduction for Gas Transportation Networks." In Simulation-Driven Modeling and Optimization, 1–28. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27517-8_1.
Повний текст джерелаLiu, Wing Kam, Zhengtao Gan, and Mark Fleming. "Knowledge-Driven Dimension Reduction and Reduced Order Surrogate Models." In Mechanistic Data Science for STEM Education and Applications, 131–70. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87832-0_5.
Повний текст джерелаAguado, Jose Vicente, Domenico Borzacchiello, Elena Lopez, Emmanuelle Abisset-Chavanne, David Gonzalez, Elias Cueto, and Francisco Chinesta. "New Trends in Computational Mechanics: Model Order Reduction, Manifold Learning and Data-Driven." In From Microstructure Investigations to Multiscale Modeling, 239–66. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2017. http://dx.doi.org/10.1002/9781119476757.ch9.
Повний текст джерелаZdybał, K., M. R. Malik, A. Coussement, J. C. Sutherland, and A. Parente. "Reduced-Order Modeling of Reacting Flows Using Data-Driven Approaches." In Lecture Notes in Energy, 245–78. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-16248-0_9.
Повний текст джерелаFerranti, Francesco, Dirk Deschrijver, Luc Knockaert, and Tom Dhaene. "Data-Driven Parameterized Model Order Reduction Using z-Domain Multivariate Orthonormal Vector Fitting Technique." In Lecture Notes in Electrical Engineering, 141–48. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-0089-5_7.
Повний текст джерелаTezzele, Marco, Nicola Demo, Andrea Mola, and Gianluigi Rozza. "An integrated data-driven computational pipeline with model order reduction for industrial and applied mathematics." In Mathematics in Industry, 179–200. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96173-2_7.
Повний текст джерелаRosafalco, Luca, Matteo Torzoni, Andrea Manzoni, Stefano Mariani, and Alberto Corigliano. "A Self-adaptive Hybrid Model/data-Driven Approach to SHM Based on Model Order Reduction and Deep Learning." In Structural Integrity, 165–84. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81716-9_8.
Повний текст джерелаТези доповідей конференцій з теми "Data-driven model order reduction"
Fischer, P., K. Kaneko, and P. Tsai. "Model-Order Reduction of Buoyancy-Driven Heat-Transfer." In Tranactions - 2019 Winter Meeting. AMNS, 2019. http://dx.doi.org/10.13182/t31297.
Повний текст джерелаSaha, Sudipta, and M. Nabi. "Model Order Reduction of Axial Active Magnetic Bearing." In 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence). IEEE, 2019. http://dx.doi.org/10.1109/confluence.2019.8776931.
Повний текст джерелаChelidze, David. "Smooth Robust Subspace Based Model Reduction." In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-13333.
Повний текст джерелаAbbasi, Mohammad Hossein, and Nathan Van de Wouw. "Model Order Reduction of Linear Sampled-Data Control Systems." In 2022 European Control Conference (ECC). IEEE, 2022. http://dx.doi.org/10.23919/ecc55457.2022.9837993.
Повний текст джерелаAnand, N. Vijay, M. Siva Kumar, and R. Srinivasa Rao. "Evolutionary Algorithm Based Model Order Reduction of MIMO Interval Systems." In Smart Technologies in Data Science and Communication 2017. Science & Engineering Research Support soCiety, 2017. http://dx.doi.org/10.14257/astl.2017.147.36.
Повний текст джерелаSaito, Akira. "Model Order Reduction for a Piecewise Linear System Based on Dynamic Mode Decomposition." In ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/detc2021-70764.
Повний текст джерелаKovárnová, A., and M. Isoz. "Model Order Reduction for Particle-Laden Flows: Systems with Rotations and Discrete Transport Operators." In Topical Problems of Fluid Mechanics 2023. Institute of Thermomechanics of the Czech Academy of Sciences; CTU in Prague Faculty of Mech. Engineering Dept. Tech. Mathematics, 2023. http://dx.doi.org/10.14311/tpfm.2023.014.
Повний текст джерелаIto, Hiroshi, Shoichiro Hosomi, Norikazu Tezuka, and Tomohiro Ishida. "On Virtual Clearance Monitoring of Steam Turbine by Using Model Order Reduction." In ASME Turbo Expo 2021: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/gt2021-59003.
Повний текст джерелаSullivan, Christopher C., Hiroki Yamashita, and Hiroyuki Sugiyama. "POD-Based Model Order Reduction for Tire-Soil Interaction Simulations." In ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/detc2021-69652.
Повний текст джерелаFan, Guodong, and Marcello Canova. "Model Order Reduction of Electrochemical Batteries Using Galerkin Method." In ASME 2015 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/dscc2015-9788.
Повний текст джерелаЗвіти організацій з теми "Data-driven model order reduction"
Fytanidis, Dimitrios, Romit Maulik, Ramesh Balakrishnan, and Rao Kotamarthi. A physics-informed data-driven low order model for the wind velocity deficit at the wake of isolated buildings. Office of Scientific and Technical Information (OSTI), March 2021. http://dx.doi.org/10.2172/1782670.
Повний текст джерелаMeidani, Hadi, and Amir Kazemi. Data-Driven Computational Fluid Dynamics Model for Predicting Drag Forces on Truck Platoons. Illinois Center for Transportation, November 2021. http://dx.doi.org/10.36501/0197-9191/21-036.
Повний текст джерелаPerera, Duminda, Ousmane Seidou, Jetal Agnihotri, Mohamed Rasmy, Vladimir Smakhtin, Paulin Coulibaly, and Hamid Mehmood. Flood Early Warning Systems: A Review Of Benefits, Challenges And Prospects. United Nations University Institute for Water, Environment and Health, August 2019. http://dx.doi.org/10.53328/mjfq3791.
Повний текст джерелаKim, Changmo, Ghazan Khan, Brent Nguyen, and Emily L. Hoang. Development of a Statistical Model to Predict Materials’ Unit Prices for Future Maintenance and Rehabilitation in Highway Life Cycle Cost Analysis. Mineta Transportation Institute, December 2020. http://dx.doi.org/10.31979/mti.2020.1806.
Повний текст джерелаZhang, Renduo, and David Russo. Scale-dependency and spatial variability of soil hydraulic properties. United States Department of Agriculture, November 2004. http://dx.doi.org/10.32747/2004.7587220.bard.
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