Relevant bibliographies by topics / Optimization via simulation

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Optimization via simulation'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Optimization via simulation"

1

Bartz-Beielstein, Thomas, Martin Zaefferer, and Quoc Cuong Pham. "Optimization via multimodel simulation." Structural and Multidisciplinary Optimization 58, no. 3 (2018): 919–33. http://dx.doi.org/10.1007/s00158-018-1934-2.

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Fu, Michael C. "Optimization via simulation: A review." Annals of Operations Research 53, no. 1 (1994): 199–247. http://dx.doi.org/10.1007/bf02136830.

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Hong, L. Jeff, and Barry L. Nelson. "Discrete Optimization via Simulation Using COMPASS." Operations Research 54, no. 1 (2006): 115–29. http://dx.doi.org/10.1287/opre.1050.0237.

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Wald, Andrew, Mark Harmon, and Diego Klabjan. "Structured deplaning via simulation and optimization." Journal of Air Transport Management 36 (April 2014): 101–9. http://dx.doi.org/10.1016/j.jairtraman.2014.01.001.

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Cusick, T. W. "Airfield simulation with optimization via decomposition." Mathematical and Computer Modelling 39, no. 6-8 (2004): 745–58. http://dx.doi.org/10.1016/s0895-7177(04)90552-3.

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Talpaz, H., and A. B. Hearn. "Calibration of Simulation Models via Nonlinear Optimization." IFAC Proceedings Volumes 21, no. 9 (1988): 1373–75. http://dx.doi.org/10.1016/s1474-6670(17)54920-4.

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Safizadeh, M. Hossein, and Robert Signorile. "Optimization of Simulation via Quasi-Newton Methods." ORSA Journal on Computing 6, no. 4 (1994): 398–408. http://dx.doi.org/10.1287/ijoc.6.4.398.

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Pichitlamken, Juta, and Barry L. Nelson. "A combined procedure for optimization via simulation." ACM Transactions on Modeling and Computer Simulation 13, no. 2 (2003): 155–79. http://dx.doi.org/10.1145/858481.858485.

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Chen, Shih-Pin. "Simulation response optimization via an alternating variable methodsimulation response optimization." Engineering Optimization 35, no. 6 (2003): 675–84. http://dx.doi.org/10.1080/03052150310001620722.

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Wing, Robyn, Michael P. Goldman, Monica M. Prieto, et al. "Usability Testing Via Simulation." Pediatric Emergency Care 40, no. 8 (2024): 575–81. http://dx.doi.org/10.1097/pec.0000000000003223.

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Objectives To inform development of a preintubation checklist for pediatric emergency departments via multicenter usability testing of a prototype checklist. Methods This was a prospective, mixed methods study across 7 sites in the National Emergency Airway Registry for Pediatric Emergency Medicine (NEAR4PEM) collaborative. Pediatric emergency medicine attending physicians and senior fellows at each site were first oriented to a checklist prototype, including content previously identified using a modified Delphi approach. Each site used the checklist in 2 simulated cases: an “easy airway” and
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Dissertations / Theses on the topic "Optimization via simulation"

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Park, Chuljin. "Discrete optimization via simulation with stochastic constraints." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/49088.

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In this thesis, we first develop a new method called penalty function with memory (PFM). PFM consists of a penalty parameter and a measure of constraint violation and it converts a discrete optimization via simulation (DOvS) problem with stochastic constraints into a series of DOvS problems without stochastic constraints. PFM determines a penalty of a visited solution based on past results of feasibility checks on the solution. Specifically, assuming a minimization problem, a penalty parameter of PFM, namely the penalty sequence, diverges to infinity for an infeasible solution but converges to
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Kabirian, Alireza. "Continuous optimization via simulation using Golden Region search." [Ames, Iowa : Iowa State University], 2009.

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Howell, William Casey. "Simulation optimization of traffic light signal timings via perturbation analysis." College Park, Md. : University of Maryland, 2006. http://hdl.handle.net/1903/3779.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2006.<br>Thesis research directed by: Applied Mathematics and Scientific Computation Program. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Villarreal-Marroquin, Maria G. "A Metamodel based Multiple Criteria Optimization via Simulation Method for Polymer Processing." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1356518813.

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Brunet, Solé Robert. "Optimal design of sustainable chemical processes via a combined simulation-optimization approach." Doctoral thesis, Universitat Rovira i Virgili, 2012. http://hdl.handle.net/10803/108997.

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Resum La societat es cada dia més conscient de la escassetat i els canvis ambientals. Per això les empreses químiques han d’adaptar i desarrollar processos químics més sostenibles. En aquest sentit, hi ha una clara demanda dins de la comunitat científica per desenvolupar eines sistemàtiques per aconseguir reduccions en els costos de producció i en l’impacte ambiental en els processos químics. Aquesta Tesis introdueix un nou mecanisme pel disseny de processos químics més sostenibles. El sistema que es presenta està basat en l’ús combinat de simulació de processos, eines d’optimització multi-o
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Yang, Dan. "Power system dynamic security analysis via decoupled time domain simulation and trajectory optimization." [Ames, Iowa : Iowa State University], 2006.

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Chen, Chia-Jung. "OPTIMIZATION OF MECHANICAL PROPERTIES IN A356 VIA SIMULATION AND PERMANENT MOLD TEST-BARS." Case Western Reserve University School of Graduate Studies / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=case1386184784.

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Bolin, Christopher E. (Christopher Eric). "Iterative uncertainty reduction via Monte Carlo simulation : a streamlined life cycle assessment case study." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/82189.

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Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2013.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>"June 2013." Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (p. 97-103).<br>Life cycle assessment (LCA) is one methodology for assessing a product's impact on the environment. LCA has grown in popularity recently as consumers and governments request more information concerning the environm
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Noyes, Clay W. "Analysis and optimization of the Emergency Department at Beth Israel Deaconess Medical Center via simulation." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45396.

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Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2008.<br>Includes bibliographical references (p. 65-67).<br>We develop a simulation model based on patient data from 2/1/05 to 1/31/06 that represents the operations of the Emergency Department at Beth Israel Deaconess Medical Center, a Harvard teaching hospital and a leading medical institution. The model uses a multiclass representation of patients, a time-varying arrival process module that uses multivariate regression to predict future patient arrivals, and a service module that ta
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Criscuolo, Ivan. "Optimization of SI and CI engine control strategies via integrated simulation of combustion and turbocharging." Doctoral thesis, Universita degli studi di Salerno, 2013. http://hdl.handle.net/10556/902.

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2010 - 2011<br>Combustion engines have been for a long time the most important prime mover for transportation globally. A combustion engine is simple in its nature; a mix of fuel and air is combusted, and work is produced in the operating cycle. The amount of combusted air and fuel controls the amount of work the engine produces. The engine work has to overcome friction and pumping losses, and a smaller engine has smaller losses and is therefore more efficient. Increasing engine efficiency in this way is commonly referred to as downsizing. Downsizing has an important disadvantage; a smal
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Books on the topic "Optimization via simulation"

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1940-, Jäger W., Rannacher Rolf, and Warnatz J, eds. Reactive flows, diffusion and transport: From experiments via mathematical modeling to numerical simulation and optimization : final report of SFB (Collaborative Research Center) 359. Springer, 2007.

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Zhao, Peiying. Optimization of an energy dispersive x-ray diffraction system via GEANT4 simulations. Laurentian University, School of Graduate Studies, 2007.

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Pachamanova, Dessislava A. Simulation and optimization in finance: Modeling with MATLAB, @Risk, or VBA. Wiley, 2010.

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Reactive Flows, Diffusion and Transport: From Experiments Via Mathematical Modeling to Numerical Simulation and Optimization. Springer London, Limited, 2007.

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Warnatz, J., Rolf Rannacher, and Willi Jäger. Reactive Flows, Diffusion and Transport: From Experiments via Mathematical Modeling to Numerical Simulation and Optimization. Springer, 2016.

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(Editor), Willi Jäger, Rolf Rannacher (Editor), and Jürgen Warnatz (Editor), eds. Reactive Flows, Diffusion and Transport: From Experiments via Mathematical Modeling to Numerical Simulation and Optimization. Springer, 2006.

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Fabozzi, Frank J., and Dessislava Pachamanova. Simulation and Optimization in Finance: Modeling with MATLAB, @RISK, or VBA. Wiley & Sons, Incorporated, John, 2010.

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Fabozzi, Frank J., and Dessislava A. Pachamanova. Simulation and Optimization in Finance: Modeling with MATLAB, @Risk, or VBA. Wiley & Sons, Incorporated, John, 2010.

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Fabozzi, Frank J., and Dessislava A. Pachamanova. Simulation and Optimization in Finance: Modeling with MATLAB, @Risk, or VBA. Wiley & Sons, Incorporated, John, 2010.

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Fabozzi, Frank J., and Dessislava A. Pachamanova. Simulation and Optimization in Finance: Modeling with MATLAB, @Risk, or VBA. Wiley & Sons, Incorporated, John, 2010.

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Book chapters on the topic "Optimization via simulation"

1

Xu, Jie. "Simulation Optimization: Discrete Optimization via Simulation." In Encyclopedia of Optimization. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-54621-2_831-1.

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Levy, Adam B. "Basin Analysis via Simulation." In SpringerBriefs in Optimization. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04049-9_5.

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Hong, L. Jeff, Barry L. Nelson, and Jie Xu. "Discrete Optimization via Simulation." In Handbook of Simulation Optimization. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-1384-8_2.

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Gosavi, Abhijit. "Solving Markov Decision Processes via Simulation." In Handbook of Simulation Optimization. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-1384-8_13.

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Noronha, Rodrigo Possidônio. "Indirect Adaptive Inverse Control Synthesis via Fractional Least Mean Square Algorithm." In Modeling, Simulation and Optimization. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0836-1_37.

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Noronha, Rodrigo Possidônio. "Direct Adaptive Inverse Control Based on Nonlinear Volterra Model via Fractional LMS Algorithm." In Modeling, Simulation and Optimization. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0836-1_36.

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Noronha, Rodrigo Possidônio. "Chaotic Lorenz Time Series Prediction via NLMS Algorithm with Fuzzy Adaptive Step Size." In Modeling, Simulation and Optimization. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0836-1_34.

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Cordasco, Gennaro, Matteo D’Auria, Carmine Spagnuolo, and Vittorio Scarano. "Heterogeneous Scalable Multi-languages Optimization via Simulation." In Communications in Computer and Information Science. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2853-4_13.

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Behera, Manoja Kumar, Lalit Chandra Saikia, Satish Kumar Ramoji, Biswanath Dekaraja, Sanjeev Kumar Bhagat, and Naladi Ram Babu. "A Novel Effective Single Sensor MPPT Technique for a Uniform and Partially Shaded Solar PV System via MSCA Approach." In Modeling, Simulation and Optimization. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9829-6_19.

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Manhardt, P. D., and A. J. Baker. "Permanent Mold Aluminium Wheel Casting Optimization Via a Finite Element Simulation." In Automotive Simulation ’91. Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-84586-4_5.

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Conference papers on the topic "Optimization via simulation"

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Mathur, Anant, Sarat Moka, Benoit Liquet, and Zdravko Botev. "Group Combss: Group Selection Via Continuous Optimization." In 2024 Winter Simulation Conference (WSC). IEEE, 2024. https://doi.org/10.1109/wsc63780.2024.10838770.

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Jeon, Yongseok, and Sara Shashaani. "Calibrating Digital Twins via Bayesian Optimization with a Root Finding Strategy." In 2024 Winter Simulation Conference (WSC). IEEE, 2024. https://doi.org/10.1109/wsc63780.2024.10838781.

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Bandi, Saurav, Sabahattin G. Ozden, Omar Ashour, and Ashkan Negahban. "The Impact of Immersion Level when Learning Optimization Concepts Via a Simulation Game." In 2024 Winter Simulation Conference (WSC). IEEE, 2024. https://doi.org/10.1109/wsc63780.2024.10838881.

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Das, Ghanendra, Kai James, Graeme Kennedy, Lonnie Webb, and Daniel Oluwalana. "Towards Crash-Tolerant Rotorcraft Design via Topology Optimization." In Vertical Flight Society 81st Annual Forum and Technology Display. The Vertical Flight Society, 2025. https://doi.org/10.4050/f-0081-2025-232.

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We present our ongoing efforts towards the development of crash-tolerant rotorcraft airframe structures through topology optimization, with the goal of enhancing energy absorption and occupant survival during vertical impact events. A high strain rate explicit dynamics solver has been developed, fully accelerated on GPUs, to enable rapid and accurate simulation of impact events critical to crashworthiness evaluation. In parallel, we have built a scalable three-dimensional topology optimization framework that enforces stiffness, weight, and frequency constraints simultaneously, driving structur
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Yuan, Ziang, Yinghui Li, and Junwei Duan. "Water Surface Autonomus Navigation Simulation via Multi-agent Proximal Policy Optimization." In 2024 International Conference on Intelligent Robotics and Automatic Control (IRAC). IEEE, 2024. https://doi.org/10.1109/irac63143.2024.10871797.

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Kang, Hyunjae, Haram Ko, Hyunju Song, et al. "Enhancing overlay metrology accuracy via E-beam simulation and SEM target optimization." In Metrology, Inspection, and Process Control XXXIX, edited by Matthew J. Sendelbach and Nivea G. Schuch. SPIE, 2025. https://doi.org/10.1117/12.3051701.

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Waag, Friedrich, and Christoph Wieland. "FROM CARBON DIOXIDE TO METHANE VIA CRYSTALLIZED HYDROGEN CARBONATE." In 37th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS 2024). ECOS 2024, 2024. http://dx.doi.org/10.52202/077185-0132.

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Pasieka, Engelbert, and Sebastian Engell. "Evolutionary Algorithm Based Real-time Scheduling via Simulation-Optimization for Multiproduct Batch Plants." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.183349.

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Production scheduling in the process industry is an area of significant activity in research and of great practical importance for the performance of industrial companies. In the vast majority of research papers, the scheduling problem is formulated as an off-line problem where a number of jobs is scheduled on a number of resources and the efficiency of the formulation and the solution algorithms is discussed. In reality, however, scheduling is a continuous activity that has to react to the arrival of new orders, to variations in processing times, breakdowns, lack of resources etc. This is ter
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Liu, Yingfang, Zhendong Sun, and Zonghai Chen. "Optimization of Fuel Cell Hybrid System Configuration via Modified Multi-Objective Particle Swarm Algorithm." In 2024 IEEE 25th China Conference on System Simulation Technology and its Application (CCSSTA). IEEE, 2024. http://dx.doi.org/10.1109/ccssta62096.2024.10691839.

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Li, Dongyang, Haitao Liu, Xiao Jin, et al. "Multi-Fidelity Discrete Optimization via Simulation." In 2022 Winter Simulation Conference (WSC). IEEE, 2022. http://dx.doi.org/10.1109/wsc57314.2022.10015380.

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Reports on the topic "Optimization via simulation"

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Glynn, Peter W. Optimization of Stochastic Systems via Simulation. Defense Technical Information Center, 1989. http://dx.doi.org/10.21236/ada214011.

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Shen, Siqian. Final Technical Report of DE-SC0018018 "Extreme-scale stochastic optimization and simulation via learning-enhanced decomposition and parallelization". Office of Scientific and Technical Information (OSTI), 2024. https://doi.org/10.2172/2341597.

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Petzke, Jonas, Dennis Kleinschmidt, and Florian Brüning. Simulative approach for predicting the heating behavior of elastomers in the solid-state microwave heating process. Universidad de los Andes, 2024. https://doi.org/10.51573/andes.pps39.gs.ms.2.

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The increasing demand for energy efficient vulcanization of rubber extrusions requires the optimization and further development of existing processes. Microwave vulcanization allows the energy required for this process to be coupled directly into the material via dielectric losses. Microwave heating requires the polarity of the rubber so that the electromagnetic wave can cause the polar components of the material to vibrate. These vibrations cause internal friction, resulting in an increase in the temperature of the rubber compound. In this research project, microwaves were used to heat a rubb
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Rémy, Elisabeth, Romain Escudier, and Alexandre Mignot. Access impact of observations. EuroSea, 2023. http://dx.doi.org/10.3289/eurosea_d4.8.

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The accuracy of the Copernicus Marine Environment and Monitoring Service (CMEMS) ocean analysis and forecasts highly depend on the availability and quality of observations to be assimilated. In situ observations are complementary to satellite observations that are restricted to the ocean surface. Higher resolution model forecasts are required by users of the CMEMS global and regional ocean analysis and forecasts. To support this with an efficient observational constrain of the model forecast via data assimilation, an increase observation coverage is needed, associated with an improved usage of
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