Relevant bibliographies by topics / Multi-fidelity models

Academic literature on the topic 'Multi-fidelity models'

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Published: 29 August 2023

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Journal articles on the topic "Multi-fidelity models"

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

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

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Multi-fidelity modelling enables accurate inference of quantities of interest by synergistically combining realizations of low-cost/low-fidelity models with a small set of high-fidelity observations. This is particularly effective when the low- and high-fidelity models exhibit strong correlations, and can lead to significant computational gains over approaches that solely rely on high-fidelity models. However, in many cases of practical interest, low-fidelity models can only be well correlated to their high-fidelity counterparts for a specific range of input parameters, and potentially return wrong trends and erroneous predictions if probed outside of their validity regime. Here we put forth a probabilistic framework based on Gaussian process regression and nonlinear autoregressive schemes that is capable of learning complex nonlinear and space-dependent cross-correlations between models of variable fidelity, and can effectively safeguard against low-fidelity models that provide wrong trends. This introduces a new class of multi-fidelity information fusion algorithms that provide a fundamental extension to the existing linear autoregressive methodologies, while still maintaining the same algorithmic complexity and overall computational cost. The performance of the proposed methods is tested in several benchmark problems involving both synthetic and real multi-fidelity datasets from computational fluid dynamics simulations.
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Rumpfkeil, Markus P., Dean Bryson, and Phil Beran. "Multi-Fidelity Sparse Polynomial Chaos and Kriging Surrogate Models Applied to Analytical Benchmark Problems." Algorithms 15, no. 3 (March 21, 2022): 101. http://dx.doi.org/10.3390/a15030101.

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In this article, multi-fidelity kriging and sparse polynomial chaos expansion (SPCE) surrogate models are constructed. In addition, a novel combination of the two surrogate approaches into a multi-fidelity SPCE-Kriging model will be presented. Accurate surrogate models, once obtained, can be employed for evaluating a large number of designs for uncertainty quantification, optimization, or design space exploration. Analytical benchmark problems are used to show that accurate multi-fidelity surrogate models can be obtained at lower computational cost than high-fidelity models. The benchmarks include non-polynomial and polynomial functions of various input dimensions, lower dimensional heterogeneous non-polynomial functions, as well as a coupled spring-mass-system. Overall, multi-fidelity models are more accurate than high-fidelity ones for the same cost, especially when only a few high-fidelity training points are employed. Full-order PCEs tend to be a factor of two or so worse than SPCES in terms of overall accuracy. The combination of the two approaches into the SPCE-Kriging model leads to a more accurate and flexible method overall.
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DiazDelaO, F. A., and S. Adhikari. "Bayesian assimilation of multi-fidelity finite element models." Computers & Structures 92-93 (February 2012): 206–15. http://dx.doi.org/10.1016/j.compstruc.2011.11.002.

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

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Bonomo, Anthony L. "Multi-fidelity surrogate modeling for structural acoustics applications." Journal of the Acoustical Society of America 153, no. 3_supplement (March 1, 2023): A287. http://dx.doi.org/10.1121/10.0018869.

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Recently, surrogate modeling methods have been explored for structural acoustics applications. These often involve evaluation of an “expensive” high-fidelity computational model to obtain training data. However, in many applications, models of varying fidelity and computational cost are available. In such situations, one can leverage multi-fidelity surrogate modeling, where the training data from models of varying fidelity are combined and simultaneously used to produce a surrogate model. A particularly popular class of multi-fidelity surrogate modeling techniques is known as co-Kriging, where simulation output from both “expensive” and “cheap” computational models are correlated and a correction process is obtained that maps between the results of these models of varying fidelity. This talk will review co-Kriging and demonstrate its utility on a canonical structural acoustics problem. [Work supported by the Office of Naval Research.]
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Peart, Tanya, Nicolas Aubin, Stefano Nava, John Cater, and Stuart Norris. "Selection of Existing Sail Designs for Multi-Fidelity Surrogate Models." Journal of Sailing Technology 7, no. 01 (January 5, 2022): 31–51. http://dx.doi.org/10.5957/jst/2022.7.2.31.

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Velocity Prediction Programs (VPPs) are commonly used to help predict and compare the performance of different sail designs. A VPP requires an aerodynamic input force matrix which can be computationally expensive to calculate, limiting its application in industrial sail design projects. The use of multi-fidelity kriging surrogate models has previously been presented by the authors to reduce this cost, with high-fidelity data for a new sail being modelled and the low-fidelity data provided by data from existing, but different, sail designs. The difference in fidelity is not due to the simulation method used to obtain the data, but instead how similar the sail’s geometry is to the new sail design. An important consideration for the construction of these models is the choice of low-fidelity data points, which provide information about the trend of the model curve between the high-fidelity data. A method is required to select the best existing sail design to use for the low-fidelity data when constructing a multi-fidelity model. The suitability of an existing sail design as a low fidelity model could be evaluated based on the similarity of its geometric parameters with the new sail. It is shown here that for upwind jib sails, the similarity of the broadseam between the two sails best indicates the ability of a design to be used as low-fidelity data for a lift coefficient surrogate model. The lift coefficient surrogate model error predicted by the regression is shown to be close to 1% of the lift coefficient surrogate error for most points. Larger discrepancies are observed for a drag coefficient surrogate error regression.
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Peart, Tanya, Nicolas Aubin, Stefano Nava, John Cater, and Stuart Norris. "Multi-Fidelity Surrogate Models for VPP Aerodynamic Input Data." Journal of Sailing Technology 6, no. 01 (February 9, 2021): 21–43. http://dx.doi.org/10.5957/jst/2021.6.1.21.

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Predicting the performance of a sail design is important for optimising the performance of a yacht, and Velocity Prediction Programs (VPPs) are commonly used for this purpose. The aerodynamic force data for a VPP is often calculated using Computational Fluid Dynamics (CFD) models, but these can be computationally expensive. A full VPP analysis for sail design is therefore usually restricted to high-budget design projects or research activities and is not practical for many industry projects. This work presents a method to reduce the computational cost of creating lift and drag force coefficient curves for input into a VPP using both multi-fidelity kriging surrogate modelling and data from existing sail designs. This method is shown to reduce the number of CFD simulations required for a desired accuracy when compared to a single-fidelity model. A maximum reduction in the required computational effort of 57% was achieved for model-scale symmetric spinnaker sails. For the same number of simulations, the accuracy of the model predictions was improved by up to 72% for scale-symmetric spinnaker sails, and 90% for asymmetric spinnakers.
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Farcaș, Ionuț-Gabriel, Benjamin Peherstorfer, Tobias Neckel, Frank Jenko, and Hans-Joachim Bungartz. "Context-aware learning of hierarchies of low-fidelity models for multi-fidelity uncertainty quantification." Computer Methods in Applied Mechanics and Engineering 406 (March 2023): 115908. http://dx.doi.org/10.1016/j.cma.2023.115908.

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

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Robot navigation through non-uniform environments requires reliable motion plan generation. The choice of planning model fidelity can significantly impact performance. Prior research has shown that reducing model fidelity saves planning time, but sacrifices execution reliability. While current adaptive hierarchical motion planning techniques are promising, we present a framework that leverages a richer set of robot motion models at plan-time. The framework chooses when to switch models and what model is most applicable within a single trajectory. For instance, more complex environment locales require higher fidelity models, while lower fidelity models are sufficient for simpler parts of the planning space, thus saving plan time. Our algorithm continuously aims to pick the model that best handles the current local environment. This effectively generates a single, mixed-fidelity plan. We present results for a simulated mobile robot with attached trailer in a hospital domain. We compare using a single motion planning model to switching with our framework of multiple models. Our results demonstrate that multi-fidelity model switching increases plan-time efficiency without sacrificing execution reliability.
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Dissertations / Theses on the topic "Multi-fidelity models"

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Benamara, Tariq. "Full-field multi-fidelity surrogate models for optimal design of turbomachines." Thesis, Compiègne, 2017. http://www.theses.fr/2017COMP2368.

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L’optimisation des différents composants d’une turbomachine reste encore un sujet épineux, malgré les récentes avancées théoriques, expérimentales ou informatiques. Cette thèse propose et investigue des techniques d’optimisation assistées par méta-modèles vectoriels multi-fidélité basés sur la Décomposition aux Valeurs Propres (POD). Le couplage de la POD à des techniques de modélisation multifidélité permet de suivre l’évolution des structures dominantes de l’écoulement en réponse à des déformations géométriques. Deux méthodes d’optimisation multi-fidélité basées sur la POD sont ici proposées. La première consiste en une stratégie d’enrichissement adaptée aux modèles multi-fidelité par Gappy-POD (GPOD). Celle-ci vise surtout des problèmes associés à des simulations basse-fidélité à coût de restitution négligeable, ce qui la rend difficilement utilisable pour la conception aérodynamique de turbomachines. Elle est néanmoins validée sur une étude du domaine de vol d’une aile 2D issue de la littérature. La seconde méthodologie est basée sur une extension multi-fidèle des modèles par POD Non-Intrusive (NIPOD). Cette extension naît de la ré-interprétation du concept de POD Contrainte (CPOD) et permet l’enrichissement de l’espace réduit par ajout important d’information basse-fidélité approximative. En seconde partie de cette thèse, un cas de validation est introduit pour valider les méthodologies d’optimisation vectorielle multi-fidélité. Cet exemple présente des caractéristiques représentatives des problèmes d’optimisation de turbomachines. La capacité de généralisation des méta-modèles par NIPOD multifidélité proposés est comparée, aussi bien sur cas analytique qu’industriel, à des techniques de méta-modélisation issues de la littérature. Enfin, nous utilisons la méthode développée au cours de cette thèse pour l’optimisation d’un étage et demi d’un compresseur basse-pression et comparons les résultats obtenus à des approches à l’état de l’art
Optimizing turbomachinery components stands as a real challenge despite recent advances in theoretical, experimental and High-Performance Computing (HPC) domains. This thesis introduces and validates optimization techniques assisted by full-field Multi-Fidelity Surrogate Models (MFSMs) based on Proper Orthogonal Decomposition (POD). The combination of POD and Multi-Fidelity Modeling (MFM) techniques allows to capture the evolution of dominant flow features with geometry modifications. Two POD based multi-fidelity optimization methods are proposed. Thefirst one consists in an enrichment strategy dedicated to Gappy-POD (GPOD)models. It is more suitable for instantaneous low-fidelity computations whichmakes it hardly tractable for aerodynamic design of turbomachines. This methodis demonstrated on the flight domain study of a 2D airfoil from the literature. The second methodology is based on a multi-fidelity extension to Non-IntrusivePOD (NIPOD) models. This extension starts with a re-interpretation of theConstrained POD (CPOD) concept and allows to enrich the reduced spacedefinition with abondant, albeit inaccurate, low-fidelity information. In the second part of the thesis, a benchmark test case is introduced to test fullfield multi-fidelity optimization methodologies on an example presenting featuresrepresentative of turbomachinery problems. The predictability of the proposedMulti-Fidelity NIPOD (MFNIPOD) surrogate models is compared to classical surrogates from the literature on both analytical and industrial-scale applications. Finally, we employ the proposed tool to the shape optimization of a 1.5-stage boosterand we compare the obtained results with standard state of the art approaches
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Belben, Joel Brian. "ENABLING RAPID CONCEPTUAL DESIGN USING GEOMETRY- BASED MULTI-FIDELITY MODELS IN VSP." DigitalCommons@CalPoly, 2013. https://digitalcommons.calpoly.edu/theses/969.

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The purpose of this work is to help bridge the gap between aircraft conceptual design and analysis. Much work is needed, but distilling essential characteristics from a design and collecting them in an easily accessible format that is amenable to use by inexpensive analysis tools is a significant contribution to this goal. Toward that end, four types of reduced-fidelity or degenerate geometric representations have been defined and implemented in VSP, a parametric geometry modeler. The four types are degenerate surface, degenerate plate, degenerate stick, and degenerate point, corresponding to three-, two-, one-, and zero- dimensional representations of underlying geometry, respectively. The information contained in these representations was targeted specifically at lifting line, vortex lattice, equivalent beam, and equivalent plate theories, with the idea that suitability for interface with these methods would imply suitability for use with many other analysis techniques. The ability to output this information in two plain text formats— comma separated value and Matlab script—has also been implemented in VSP, making it readily available for use. A modified Cessna 182 wing created in VSP was used to test the suitability of degenerate geometry to interface with the four target analysis techniques. All four test cases were easily completed using the information contained in the degenerate geometric types, and similar techniques utilizing different degenerate geometries produced similar results. The following work outlines the theoretical underpinnings of degenerate geometry and the fidelity-reduction process. It also describes in detail how the routines that create degenerate geometry were implemented in VSP and concludes with the analysis test cases, stating their results and comparing results among different techniques.
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Hebert, James L. "Use of Multi-Fidelity and Surrogate Models to Reduce the Cost of Developing Physics-Based Systems." Thesis, The George Washington University, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3687685.

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Building complex physics-based systems in a timely cost-effective manner, that perform well, meet diverse user needs, and have no bad emergent behaviors is a challenge. To meet these requirements the solution is to model the physics-based system before building it. Modeling and Simulation capabilities for these type systems have advanced continuously during the past 20 years thanks to progress in the application of high fidelity computational codes that are able to model the real physical performance of system components. The problem is that it is often too time consuming and costly to model complex systems, end-to-end, using these high fidelity computational models alone. Missing are good approaches to segment the modeling of complex systems performance and behaviors, keep the model chain coherent and only model what is necessary. Current research efforts have shown that using multi-fidelity and/or surrogate models might offer alternative methods of performing the modeling and simulations needed to design and develop physics-based systems more efficiently. This study demonstrates that it is possible reduce the number of high fidelity runs allowing the use of classical systems engineering analysis and tools that would not be possible if only high fidelity codes were employed. This study advances the systems engineering of physics-based systems by reducing the number of time consuming high fidelity models and simulations that must be used to design and develop the systems. The study produced a novel approach to the design and development of complex physics-based systems by using a mix of variable fidelity physics-based models and surrogate models. It shows that this combination of increasing fidelity models enables the computationally and cost efficient modeling and simulation of these complex systems and their components. The study presents an example of the methodology for the analysis and design of two physics-based systems: a Ground Penetrating Radar (GPR) and a Nuclear Electromagnetic Pulse Bounded Wave System.

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Raub, Corey Bevan. "Geometric analysis of axisymmetric disk forging." Ohio : Ohio University, 2000. http://www.ohiolink.edu/etd/view.cgi?ohiou1172778393.

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Aulmann, Maria. "Entwicklung und Evaluierung von Clinical Skills - Simulatoren für die Lehre in der Tiermedizin." Doctoral thesis, Universitätsbibliothek Leipzig, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-215224.

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Einleitung Studierende der Veterinärmedizin müssen neben umfangreichem theoretischem Wissen zahlreiche praktische Fertigkeiten erlernen. Da jeder Einzelne in seinem eigenen Tempo lernt, besteht ein großer Bedarf an Trainingsmöglichkeiten. Kadaver und lebende Tiere sind selten in ausreichender Menge verfügbar und lebende Tiere sind zudem aus Gründen des Tierwohls nur eingeschränkt zu verwenden. Simulationsmodelle (Modelle von Organismen / Körperteilen) können hier Abhilfe schaffen. Kommerziell erhältliche Modelle sind sehr kostenintensiv und für die Tiermedizin noch nicht flächendeckend erhältlich. Zunehmend werden selbst entwickelte low-fidelity Modelle in der Tiermedizin verwendet. Aufgrund des Mangels an publizierten Daten zu ihrem Einsatz besteht intensiver Forschungsbedarf. Ziele der Untersuchungen In dieser Arbeit sollte untersucht werden, ob einfache, selbst entwickelte Simulationsmodelle (low-fidelity Modelle) erfolgreich in der Lehre eingesetzt werden können. Dazu wurden zwei selbst entwickelte und gebaute Simulationsmodelle evaluiert (Studie 1) und ihr Einsatz in Kombination mit anderen Lehrmedien untersucht (Studie 2). Materialien und Methoden In Studie 1 wurden zwei low-fidelity Modelle zur kaninen Intubation und Katheterisierung entwickelt und evaluiert. Es wurde ein Studiendesign genutzt, das die erworbenen Fertigkeiten zweier Übungsgruppen und einer Kontrollgruppe in einer praktischen Prüfung (OSCE = objective structured clinical examination) am toten Hund vergleicht. Achtundfünfzig Studierende (4. FS) erhielten eine theoretische Einführung zur Intubation und wurden randomisiert auf drei Gruppen aufgeteilt. Gruppe 1 (high-fidelity) übte am kommerziell erhältlichen Intubation Training Manikin, Gruppe 2 (low-fidelity) am entwickelten low-fidelity Modell und die Textgruppe las einen Text, der die Intubation beim Hund beschreibt. Siebenundvierzig Studierende (10. FS) durchliefen dasselbe Studiendesign zum Thema Katheterisierung der Hündin. Sie nutzten das kommerziell erhältliche Female Urinary Catheter Training Manikin, das selbst entwickelte low-fidelity Modell und Lehrtexte. In Studie 2 wurde die Vermittlung zweier spezifischer Fertigkeiten mit Hilfe von Potcasts und Simulationstraining evaluiert. Zwei anleitende Potcasts zu Intubation und Katheterisierung und die oben beschriebenen Modelle wurden innerhalb eines crossover-Studiendesigns genutzt. In dieser Studie sind Potcasts audio-visuell aufbereitete Animationen mit Schritt für Schritt – Anleitungen und Informationen. Die erworbenen praktischen Fertigkeiten zweier Übungsgruppen, die sich in der Art der theoretischen Vorbereitung unterschieden, wurden in einer praktischen Prüfung (OSCE) am toten Hund verglichen. Ein Fragebogen erfasste das Feedback der Teilnehmer. Sechzig Studierende (2. FS) wurden randomisiert auf eine Potcast- und eine Textgruppe aufgeteilt. Die Potcastgruppe sah sich das anleitende Potcast an, die Textgruppe bereitete sich anhand eines Lehrtextes vor. Im Anschluss hatten beide Gruppen separate Übungseinheiten an den low-fidelity Modellen ohne Betreuung durch Lehrende. Ergebnisse In Studie 1 schnitten alle Übungsgruppen signifikant besser ab als die Textgruppen. Gruppe 1 (high-fidelity) und Gruppe 2 (low-fidelity) unterschieden sich weder bei der Intubation noch bei der Katheterisierung signifikant in ihren Leistungen. In Studie 2 schnitt die Potcastgruppe beim Thema Intubation signifikant besser ab als die Textgruppe, beim Thema Katheterisierung ergaben sich keine signifikanten Unterschiede. Insgesamt hatte das Simulationstraining den Studierenden Spaß gemacht, das Lernen ohne Betreuer wurde jedoch als Herausforderung empfunden. Schlussfolgerungen Es ist davon auszugehen, dass low-fidelity Modelle genauso geeignet für das Training klinischer Fertigkeiten sein können wie high-fidelity Modelle. Das Training klinischer Fertigkeiten mit Hilfe von Potcasts und low-fidelity Modellen sollte durch Betreuer ergänzt werden, anstatt als alleiniges Lehrmedium für Studierende des ersten Studienjahres Verwendung zu finden. Eigenständiges Lernen klinischer Fertigkeiten, angeleitet durch Potcasts bietet eine Möglichkeit für vertiefendes und wiederholendes Training höherer Semester. Der Einsatz von Simulationsmodellen in der veterinärmedizinischen Ausbildung wächst seit wenigen Jahren stetig. Diese Arbeit leistet einen zeitgerechten Beitrag bei der Evaluierung von Simulationstraining
Introduction Students of veterinary medicine are expected to acquire various practical skills in addition to a wide range of theoretical knowledge. There is a strong demand for training opportunities, as every individual learns and acquires practical skills at individual pace. For reasons of animal welfare concerns and availability, live animals and cadavers cannot always be used for clinical skills training. Simulation models, which are models of organisms or body parts can be a considerable alternative for clinical skills training. Models that are commercially produced often have a high price and are not available for all skills. Self-made models are increasingly used in veterinary education. Because there is few published data regarding their use, more scientific research is required. Aims of the Investigation The objective of this study was to determine, if self-made low-fidelity models can be successfully used in veterinary medical education. For this purpose, two self-made low-fidelity models were evaluated (study 1) and their use in combination with other teaching tools was analyzed (study 2). Materials and Methods In study 1, two self-made low-fidelity models for simulation of canine intubation and canine female urinary catheterization were developed and evaluated. We used a study design that compares acquired skills of two intervention groups and one control group in a practical examination (OSCE = objective structured clinical examination). Fifty-eight second-year veterinary medicine students received a theoretical introduction to intubation and were randomly divided into three groups. Group 1 (high-fidelity) was then trained on a commercially available Intubation Training Manikin, group 2 (low-fidelity) was trained on our low-fidelity model, and the text group read a text describing intubation of the dog. Forty-seven fifth-year veterinary medicine students followed the same procedure for training urinary catheterization using the commercially available Female Urinary Catheter Training Manikin, our self-made model, and text. Outcomes were assessed in a practical examination on a cadaver using an OSCE checklist. In study 2 we evaluated the teaching of two specific clinical skills using potcasts and low-fidelity simulation training. Two instructional potcasts describing intubation and catheterization and both low-fidelity models described above were used. In our study, potcasts are audio-visual animations that provide the learner with step by step information and instruction on a clinical skill. We used a crossover study design and compared the acquired practical skills of two intervention groups after a different theoretical preparation. A survey captured the participants’ feedback. Sixty first year veterinary medicine students were randomly allocated to two groups, a potcast group and a text group. The potcast group watched a potcast while the text group read an instructional text for preparation. Then both groups had separate self-directed training sessions on low-fidelity models. Outcomes were assessed in practical examinations on a cadaver using an objective structured clinical examination (OSCE) checklist. Results In study 1 all intervention groups performed significantly better than the text groups. Group I (high-fidelity) and group II (low-fidelity) for both intubation and catheterization showed no significant differences. In study 2 the potcast group performed significantly better than the text group in study intubation but no significant differences were observed in study catheterization. Overall, participants enjoyed clinical skills training but experienced self-directed learning as challenging. Conclusion Low-fidelity models can be as effective as high-fidelity models for clinical skills training. Clinical skills training using potcasts and self-directed low-fidelity simulation training should be complemented by supervisor or peer instruction rather than used as exclusive tool for teaching first year veterinary students. We assume though, that self-directed learning instructed by our potcasts can be a valuable chance for deepening and repetitive training of higher semesters. The use of simulation models in veterinary education has been consistently increasing in the past few years. This study is an important, timely contribution to the evaluation of simulation based education
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Biehler, Jonas [Verfasser], Wolfgang A. [Akademischer Betreuer] [Gutachter] Wall, and Phaedon-Stelios [Gutachter] Koutsourelakis. "Efficient Uncertainty Quantification for Large-Scale Biomechanical Models Using a Bayesian Multi-Fidelity Approach / Jonas Biehler ; Gutachter: Wolfgang A. Wall, Phaedon-Stelios Koutsourelakis ; Betreuer: Wolfgang A. Wall." München : Universitätsbibliothek der TU München, 2016. http://d-nb.info/1123729220/34.

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Sacher, Matthieu. "Méthodes avancées d'optimisation par méta-modèles – Applicationà la performance des voiliers de compétition." Thesis, Paris, ENSAM, 2018. http://www.theses.fr/2018ENAM0032/document.

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L’optimisation de la performance des voiliers est un problème difficile en raison de la complexité du systèmemécanique (couplage aéro-élastique et hydrodynamique) et du nombre important de paramètres à optimiser (voiles, gréement,etc.). Malgré le fait que l’optimisation des voiliers est empirique dans la plupart des cas aujourd’hui, les approchesnumériques peuvent maintenant devenir envisageables grâce aux dernières améliorations des modèles physiques et despuissances de calcul. Les calculs aéro-hydrodynamiques restent cependant très coûteux car chaque évaluation demandegénéralement la résolution d’un problème non linéaire d’interaction fluide-structure. Ainsi, l’objectif central de cette thèseest de proposer et développer des méthodes originales dans le but de minimiser le coût numérique de l’optimisation dela performance des voiliers. L’optimisation globale par méta-modèles Gaussiens est utilisée pour résoudre différents problèmesd’optimisation. La méthode d’optimisation par méta-modèles est étendue aux cas d’optimisations sous contraintes,incluant de possibles points non évaluables, par une approche de type classification. L’utilisation de méta-modèles à fidélitésmultiples est également adaptée à la méthode d’optimisation globale. Les applications concernent des problèmesd’optimisation originaux où la performance est modélisée expérimentalement et/ou numériquement. Ces différentes applicationspermettent de valider les développements des méthodes d’optimisation sur des cas concrets et complexes, incluantdes phénomènes d’interaction fluide-structure
Sailing yacht performance optimization is a difficult problem due to the high complexity of the mechanicalsystem (aero-elastic and hydrodynamic coupling) and the large number of parameters to optimize (sails, rigs, etc.).Despite the fact that sailboats optimization is empirical in most cases today, the numerical optimization approach is nowconsidered as possible because of the latest advances in physical models and computing power. However, these numericaloptimizations remain very expensive as each simulation usually requires solving a non-linear fluid-structure interactionproblem. Thus, the central objective of this thesis is to propose and to develop original methods aiming at minimizing thenumerical cost of sailing yacht performance optimization. The Efficient Global Optimization (EGO) is therefore appliedto solve various optimization problems. The original EGO method is extended to cases of optimization under constraints,including possible non computable points, using a classification-based approach. The use of multi-fidelity surrogates isalso adapted to the EGO method. The applications treated in this thesis concern the original optimization problems inwhich the performance is modeled experimentally and/or numerically. These various applications allow for the validationof the developments in optimization methods on real and complex problems, including fluid-structure interactionphenomena
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Chetry, Manisha. "Advanced reduced-order modeling and parametric sampling for non-Newtonian fluid flows." Electronic Thesis or Diss., Ecole centrale de Nantes, 2023. http://www.theses.fr/2023ECDN0011.

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Le sujet de cette thèse porte sur laréduction d'ordre de modèle (MOR) deproblèmes d'écoulement non-Newtonianparamétrés qui ont des applicationsindustrielles importantes. Les méthodestraditionnelles de réduction de l'ordre desmodèles limitent les performances decalcul de ces problèmes hautement nonlinéaires, nous suggérons donc une techniqued'hyper-réduction avancée basée sur uneapproximation sparse de l'évaluation destermes non linéaire à complexité reduite.Nous proposons également une stratégie destabilisation hors ligne pour stabiliser le modèleconstitutif dans le modèle d'ordre réduit quiest moins cher à calculer tout en maintenant laprécision du modèle d'ordre complet. Lacombinaison des deux réduit drastiquement lecoût du processeur, augmentantinévitablement les performances du MOR. Cetravail est validé sur deux problèmes debenchmark. En outre, une stratégied'échantillonnage adaptatif est égalementprésentée dans ce manuscrit, qui est réaliséeen tirant parti de l'approximation des modèlesmulti-fidélité. Vers la fin de la thèse, nousabordons un autre problème qui estgénéralement observé dans les cas où desmaillages d'éléments finis adaptatifs sontdéployés. Dans de tels cas, les méthodes MORne parviennent pas à produire unereprésentation de faible dimension car lessnapshots ne sont pas des vecteurs de mêmelongueur. Par conséquent, nous suggérons uneméthodologie qui peut générer des fonctionsde base réduites pour des snapshotsadaptative
The subject of this thesis concernsmodel-order reduction (MOR) of parameterizednon-Newtonian flow problems that havesignificant industrial applications. TraditionalMOR methods constrain the computationalperformance of such highly nonlinear problems,so we suggest a state-of-the-art hyper-reductiontechnique based on a sparse approximation totackle the evaluation of nonlinear terms at muchreduced complexity. We also provide offlinestabilization strategy for stabilizing theconstitutive model in the reduced order modelframework that is less expensive to computewhile maintaining the full order model's (FOM)accuracy. Combining the two significantlylowers the CPU cost as compared to the FOMevaluation which inevitably boosts MORperformance. This work is validated on twobenchmark flow problems. Additionally, anadaptive sampling strategy is also presented inthis manuscript which is achieved byleveraging multi-fidelity model approximation.Towards the end of the thesis, we addressanother issue that is typically observed forcases when adaptive finite element meshesare deployed. In such cases, MOR methods failto produce a low-dimensional representationsince the snapshots are not vectors of samelength. We therefore, suggest an alternatemethod that can generate reduced basisfunctions for database of space-adaptedsnapshots
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Eaton, Ammon Nephi. "Multi-Fidelity Model Predictive Control of Upstream Energy Production Processes." BYU ScholarsArchive, 2017. https://scholarsarchive.byu.edu/etd/6376.

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Increasing worldwide demand for petroleum motivates greater efficiency, safety, and environmental responsibility in upstream oil and gas processes. The objective of this research is to improve these areas with advanced control methods. This work develops the integration of optimal control methods including model predictive control, moving horizon estimation, high fidelity simulators, and switched control techniques applied to subsea riser slugging and managed pressure drilling. A subsea riser slugging model predictive controller eliminates persistent offset and decreases settling time by 5% compared to a traditional PID controller. A sensitivity analysis shows the effect of riser base pressure sensor location on controller response. A review of current crude oil pipeline wax deposition prevention, monitoring, and remediation techniques is given. Also, industrially relevant control model parameter estimation techniques are reviewed and heuristics are developed for gain and time constant estimates for single input/single output systems. The analysis indicates that overestimated controller gain and underestimated controller time constant leads to better controller performance under model parameter uncertainty. An online method for giving statistical significance to control model parameter estimates is presented. Additionally, basic and advanced switched model predictive control schemes are presented. Both algorithms use control models of varying fidelity: a high fidelity process model, a reduced order nonlinear model, and a linear empirical model. The basic switched structure introduces a method for bumpless switching between control models in a predetermined switching order. The advanced switched controller builds on the basic controller; however, instead of a predetermined switching sequence, the advanced algorithm uses the linear empirical controller when possible. When controller performance becomes unacceptable, the algorithm implements the low order model to control the process while the high fidelity model generates simulated data which is used to estimate the empirical model parameters. Once this online model identification process is complete, the controller reinstates the empirical model to control the process. This control framework allows the more accurate, yet computationally expensive, predictive capabilities of the high fidelity simulator to be incorporated into the locally accurate linear empirical model while still maintaining convergence guarantees.
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MAININI, LAURA. "Multidisciplinary and multi-fidelity optimization environment for wing integrated design." Doctoral thesis, Politecnico di Torino, 2012. http://hdl.handle.net/11583/2500000.

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The Ph.D. program has been focused on the development of a multidisciplinary integrated environment for the design of wing for which large changes in shape are expected to be allowed during the flight in order to be better adapted for the different flight segments. The first phase of study has been dedicated to the investigation of the proper Multidisciplinary Design Optimization (MDO) architecture for the integrated management of the design process and a multilevel solution has been proposed and implemented. Such framework involves several disciplinary analysis and optimization loops: in particular aerodynamic analysis, structural analysis, material optimization and mission and performance evaluation are the main components considered for the preliminary design development for such a “morphing” wing. This stage addressed basically the multidisciplinarity and interdisciplinarity issues. The second phase has been dedicated to the investigation of possible techniques for the reduction of the computational burden that characterizes typically this kind of integrated design processes. For this purpose multi-fidelity analysis techniques have been considered involving the use of surrogate models. In particular the attention has been focused on the study of a proper methodology to build an approximated model for the estimation of aerodynamic coefficients to be used for performance evaluation in the mission optimization stage. In this case a procedure involving variables screening phase, data-fit surrogate models evaluation and assessment phase and a final crucial global correction phase of the best surrogate model has been proposed.
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Book chapters on the topic "Multi-fidelity models"

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Jiang, Ping, Qi Zhou, and Xinyu Shao. "Multi-fidelity Surrogate Models." In Surrogate Model-Based Engineering Design and Optimization, 55–87. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0731-1_4.

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Nachar, S., P. A. Boucard, D. Néron, U. Nackenhorst, and A. Fau. "Multi-fidelity Metamodels Nourished by Reduced Order Models." In Virtual Design and Validation, 61–79. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38156-1_4.

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Koziel, Slawomir, and Leifur Leifsson. "Multi-objective Optimization Using Variable-Fidelity Models and Response Correction." In Simulation-Driven Design by Knowledge-Based Response Correction Techniques, 193–210. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30115-0_11.

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Poethke, Bernhard, Stefan Völker, and Konrad Vogeler. "Aerodynamic Optimization of Turbine Airfoils Using Multi-fidelity Surrogate Models." In EngOpt 2018 Proceedings of the 6th International Conference on Engineering Optimization, 556–68. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97773-7_50.

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Peri, Daniele, Antonio Pinto, and Emilio F. Campana. "Multi-Objective Optimisation of Expensive Objective Functions with Variable Fidelity Models." In Nonconvex Optimization and Its Applications, 223–41. Boston, MA: Springer US, 2006. http://dx.doi.org/10.1007/0-387-30065-1_14.

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Leifsson, Leifur, Slawomir Koziel, Yonatan Tesfahunegn, and Adrian Bekasiewicz. "Fast Multi-Objective Aerodynamic Optimization Using Space-Mapping-Corrected Multi-Fidelity Models and Kriging Interpolation." In Simulation-Driven Modeling and Optimization, 55–73. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27517-8_3.

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Fan, Yiming, and Fotis Kopsaftopoulos. "Damage State Estimation via Multi-fidelity Gaussian Process Regression Models for Active-Sensing Structure Health Monitoring." In Lecture Notes in Civil Engineering, 267–76. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-07258-1_28.

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Schürmann, Felix, Jean-Denis Courcol, and Srikanth Ramaswamy. "Computational Concepts for Reconstructing and Simulating Brain Tissue." In Advances in Experimental Medicine and Biology, 237–59. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89439-9_10.

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AbstractIt has previously been shown that it is possible to derive a new class of biophysically detailed brain tissue models when one computationally analyzes and exploits the interdependencies or the multi-modal and multi-scale organization of the brain. These reconstructions, sometimes referred to as digital twins, enable a spectrum of scientific investigations. Building such models has become possible because of increase in quantitative data but also advances in computational capabilities, algorithmic and methodological innovations. This chapter presents the computational science concepts that provide the foundation to the data-driven approach to reconstructing and simulating brain tissue as developed by the EPFL Blue Brain Project, which was originally applied to neocortical microcircuitry and extended to other brain regions. Accordingly, the chapter covers aspects such as a knowledge graph-based data organization and the importance of the concept of a dataset release. We illustrate algorithmic advances in finding suitable parameters for electrical models of neurons or how spatial constraints can be exploited for predicting synaptic connections. Furthermore, we explain how in silico experimentation with such models necessitates specific addressing schemes or requires strategies for an efficient simulation. The entire data-driven approach relies on the systematic validation of the model. We conclude by discussing complementary strategies that not only enable judging the fidelity of the model but also form the basis for its systematic refinements.
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Schürmann, Felix, Jean-Denis Courcol, and Srikanth Ramaswamy. "Computational Concepts for Reconstructing and Simulating Brain Tissue." In Advances in Experimental Medicine and Biology, 237–59. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89439-9_10.

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AbstractIt has previously been shown that it is possible to derive a new class of biophysically detailed brain tissue models when one computationally analyzes and exploits the interdependencies or the multi-modal and multi-scale organization of the brain. These reconstructions, sometimes referred to as digital twins, enable a spectrum of scientific investigations. Building such models has become possible because of increase in quantitative data but also advances in computational capabilities, algorithmic and methodological innovations. This chapter presents the computational science concepts that provide the foundation to the data-driven approach to reconstructing and simulating brain tissue as developed by the EPFL Blue Brain Project, which was originally applied to neocortical microcircuitry and extended to other brain regions. Accordingly, the chapter covers aspects such as a knowledge graph-based data organization and the importance of the concept of a dataset release. We illustrate algorithmic advances in finding suitable parameters for electrical models of neurons or how spatial constraints can be exploited for predicting synaptic connections. Furthermore, we explain how in silico experimentation with such models necessitates specific addressing schemes or requires strategies for an efficient simulation. The entire data-driven approach relies on the systematic validation of the model. We conclude by discussing complementary strategies that not only enable judging the fidelity of the model but also form the basis for its systematic refinements.
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Rosenberg, Jonathan, Mark Sherman, Ann Marks, and Jaap Akkerhuis. "Document Models and Interchange Fidelity." In Multi-media Document Translation, 21–36. New York, NY: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4684-6404-7_2.

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Conference papers on the topic "Multi-fidelity models"

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Cambeiro, Joao, Julien Deantoni, and Vasco Amaral. "Supporting the Engineering of Multi-Fidelity Simulation Units With Simulation Goals." In 2021 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C). IEEE, 2021. http://dx.doi.org/10.1109/models-c53483.2021.00053.

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Thenon, A., V. Gervais, and M. Le Ravalec. "Multi-fidelity Proxy Models for Reservoir Engineering." In ECMOR XV - 15th European Conference on the Mathematics of Oil Recovery. Netherlands: EAGE Publications BV, 2016. http://dx.doi.org/10.3997/2214-4609.201601831.

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Frigerio, Nicla, Andrea Matta, and Ziwei Lin. "MULTI-FIDELITY MODELS FOR DECOMPOSED SIMULATION OPTIMIZATION PROBLEMS." In 2018 Winter Simulation Conference (WSC). IEEE, 2018. http://dx.doi.org/10.1109/wsc.2018.8632480.

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Nelson, Andrea, Juan Alonso, and Thomas Pulliam. "Multi-Fidelity Aerodynamic Optimization Using Treed Meta-Models." In 25th AIAA Applied Aerodynamics Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2007. http://dx.doi.org/10.2514/6.2007-4057.

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Phelivan Soak, H., J. Wackers, R. Pellegrini, A. Serani, M. Diez, R. Perali, M. Sacher, et al. "Hydrofoil Optimization via Automated Multi-Fidelity Surrogate Models." In 10th Conference on Computational Methods in Marine Engineering. CIMNE, 2023. http://dx.doi.org/10.23967/marine.2023.136.

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Pai, Sai G. S., and Ian F. C. Smith. "Multi-fidelity modelling for structural identification." In IABSE Symposium, Guimarães 2019: Towards a Resilient Built Environment Risk and Asset Management. Zurich, Switzerland: International Association for Bridge and Structural Engineering (IABSE), 2019. http://dx.doi.org/10.2749/guimaraes.2019.1092.

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<p>Asset-management decision-making is often improved by obtaining a better understanding structural behaviour through monitoring, which can then help avoid unnecessary repair, retrofit and replacement of existing infrastructure. Interpretation of monitoring data in the presence of biased and systematic uncertainties may require computationally time-consuming numerical models to approximate real structural behaviour. These models could be replaced by less time-consuming machine learning-based surrogate models. When and how this should be done is the subject of current research. In this paper, the use of surrogate models in a multi-fidelity framework for structural identification of a full-scale bridge is presented. The effects of varying degrees of fidelity are studied in a transparent manner within a structural-identification framework. The use of models with multiple fidelities helps obtain accurate model updating results in less time compared with using only one high-fidelity model class for simulations.</p>
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Pang, Bowen, Xiaolei Xie, Betnd Heidergott, and Yijie Peng. "optimizing outpatient Department Staffing Level using Multi-Fidelity Models." In 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE). IEEE, 2019. http://dx.doi.org/10.1109/coase.2019.8842984.

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Jaeggi, Daniel, Geoff Parks, William Dawes, and John Clarkson. "Robust Multi-Fidelity Aerodynamic Design Optimization Using Surrogate Models." In 12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2008. http://dx.doi.org/10.2514/6.2008-6052.

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Sendrea, Ricardo E., Constantinos L. Zekios, and Stavros V. Georgakopoulos. "A Multi-Fidelity Surrogate Optimization Method Based on Analytical Models." In 2021 IEEE/MTT-S International Microwave Symposium - IMS 2021. IEEE, 2021. http://dx.doi.org/10.1109/ims19712.2021.9574986.

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Yong, Hau Kit, Leran Wang, David J. J. Toal, Andy J. Keane, and Felix Stanley. "Multi-Fidelity Kriging-Based Optimization of Engine Subsystem Models With Medial Meshes." In ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/gt2018-76148.

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Engine subsystem models are not commonly used in design optimization studies as it is computationally expensive to solve these models for a large number of iterations. To reduce the computational cost of such optimizations, a novel multi-fidelity Kriging-based optimization approach is proposed that uses shell FEMs to provide a low-fidelity response and solid FEMs to provide a high-fidelity response. This marks the first time that shell and solid models are used together in a multi-fidelity surrogate modelling approach. The shell FEMs are generated from medial surfaces that are extracted from solid component geometries in a semi-automatic manner. This approach is applied to a case study for optimizing the intercasing subsystem from the CRESCENDO whole engine model. The results show that the optimum design found by the multi-fidelity Kriging approach was on par with the optimum design found by a single-fidelity Kriging approach using only solid FEMs which is more than twice as expensive to run. The shell and solid FEMs were also shown to be well-correlated such that optimization studies employing only the shell FEMs by themselves could generate designs that are feasible with respect to the design constraints imposed on the solid model.
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Reports on the topic "Multi-fidelity models"

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Balachandran, B. Leveraging Multi-Fidelity Models for Flexible Wing Systems. Fort Belvoir, VA: Defense Technical Information Center, May 2014. http://dx.doi.org/10.21236/ada611076.

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Maulik, Romit, Virendra Ghate, William Pringle, Yan Feng, Vishwas Rao, Julie Bessac, and Bethany Lusch. Surrogate multi-fidelity data and model fusion forscientific discovery and uncertainty quantification inEarth System Models. Office of Scientific and Technical Information (OSTI), April 2021. http://dx.doi.org/10.2172/1769781.

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Yin, Lin. IC Report for project “Developing nonlinear laser-plasma instability models for high-fidelity, multi-physics simulation capability for ICF/HED”. Office of Scientific and Technical Information (OSTI), February 2022. http://dx.doi.org/10.2172/1846881.

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Yin, Lin. IC Report for project “Developing nonlinear laser-plasma instability models for high-fidelity, multi-physics simulation capability for ICF/HED”. Office of Scientific and Technical Information (OSTI), March 2023. http://dx.doi.org/10.2172/1968190.

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Modest, Michael. AOI 1— COMPUTATIONAL ENERGY SCIENCES:MULTIPHASE FLOW RESEARCH High-fidelity multi-phase radiation module for modern coal combustion systems. Office of Scientific and Technical Information (OSTI), November 2013. http://dx.doi.org/10.2172/1134746.

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