Relevant bibliographies by topics / Mathematical optimization

Contents

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

Academic literature on the topic 'Mathematical optimization'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Mathematical optimization"

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Kulcsár, T., and I. Timár. "Mathematical optimization and engineering applications." Mathematical Modeling and Computing 3, no. 1 (2016): 59–78. http://dx.doi.org/10.23939/mmc2016.01.059.

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Bhardwaj, Suyash, Seema Kashyap, and Anju Shukla. "A Novel Approach For Optimization In Mathematical Calculations Using Vedic Mathematics Techniques." MATHEMATICAL JOURNAL OF INTERDISCIPLINARY SCIENCES 1, no. 1 (2012): 23–34. http://dx.doi.org/10.15415/mjis.2012.11002.

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Chawla, Dr Meenu. "Mathematical optimization techniques." Pharma Innovation 8, no. 2 (2019): 888–92. http://dx.doi.org/10.22271/tpi.2019.v8.i2n.25454.

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Suhl, Uwe H. "MOPS — Mathematical optimization system." European Journal of Operational Research 72, no. 2 (1994): 312–22. http://dx.doi.org/10.1016/0377-2217(94)90312-3.

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Requelme Ibáñez, Rosa María, Carlos Abel Reyes Alvarado, and Jorge Luis Lozano Cervera. "Mathematical optimization for economic agents." Revista Ciencia y Tecnología 17, no. 3 (2021): 81–89. http://dx.doi.org/10.17268/rev.cyt.2021.03.07.

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Sezer, Ali Devin, and Gerhard-Wilhelm Weber. "Optimization Methods in Mathematical Finance." Optimization 62, no. 11 (2013): 1399–402. http://dx.doi.org/10.1080/02331934.2013.863528.

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Stanojević, Milan, and Bogdana Stanojević. "Lua APIs for mathematical optimization." Procedia Computer Science 242 (2024): 460–65. http://dx.doi.org/10.1016/j.procs.2024.08.160.

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García, J. M., C. A. Acosta, and M. J. Mesa. "Genetic algorithms for mathematical optimization." Journal of Physics: Conference Series 1448 (January 2020): 012020. http://dx.doi.org/10.1088/1742-6596/1448/1/012020.

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Gorissen, Bram L., Jan Unkelbach, and Thomas R. Bortfeld. "Mathematical Optimization of Treatment Schedules." International Journal of Radiation Oncology*Biology*Physics 96, no. 1 (2016): 6–8. http://dx.doi.org/10.1016/j.ijrobp.2016.04.012.

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Feichtinger, Gustav. "Mathematical Optimization and Economic Analysis." European Journal of Operational Research 221, no. 1 (2012): 273–74. http://dx.doi.org/10.1016/j.ejor.2012.03.018.

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Dissertations / Theses on the topic "Mathematical optimization"

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Keanius, Erik. "Mathematical Optimization in SVMs." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-297492.

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In this thesis, support vector machines (SVMs) are studied from a mathematical optimization viewpoint. Both the linear case using hard-margin as well as soft-margin classification and the non-linear case using kernel functions are discussed. The theory of kernel Hilbert spaces is introduced and related to the non-linear SVM case. Moreover, fundamental theorems from optimization, including Lagrangian duality and KKT conditions, are introduced and proved. These theorems are then applied to the optimization problem of SVMs. Finally, the SVM optimization problem is implemented, solved, and visuali
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Zhou, Fangjun. "Nonmonotone methods in optimization and DC optimization of location problems." Diss., Georgia Institute of Technology, 1997. http://hdl.handle.net/1853/21777.

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Holm, Åsa. "Mathematical Optimization of HDR Brachytherapy." Doctoral thesis, Linköpings universitet, Optimeringslära, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-99795.

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One out of eight deaths throughout the world is due to cancer. Developing new treatments and improving existing treatments is hence of major importance. In this thesis we have studied how mathematical optimization can be used to improve an existing treatment method: high-dose-rate (HDR) brachytherapy. HDR brachytherapy is a radiation modality used to treat tumours of for example the cervix, prostate, breasts, and skin. In HDR brachytherapy catheters are implanted into or close to the tumour volume. A radioactive source is moved through the catheters, and by adjusting where the catheters are pl
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Najafiazar, Bahador. "Mathematical Optimization in Reservoir Management." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for petroleumsteknologi og anvendt geofysikk, 2014. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-27058.

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Getting the most out of a hydrocarbon reservoir is not a trivial task. It takes plentyof interwoven decisions to make. There are many forms of tools that support engineersto make correct decisions. The simplest ones would only display measurementsin a suitable way, and appoint the rest of the decision making processto human knowledge and experience. Complex decision support tools may implementmodel-based estimation and optimization. This work targets methods foroptimization-based decision support.The objective of this study is to formulate, implement and test promising methodsof hydrocarbon pr
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Saunders, David. "Applications of optimization to mathematical finance." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq29265.pdf.

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Chang, Tyler Hunter. "Mathematical Software for Multiobjective Optimization Problems." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/98915.

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In this thesis, two distinct problems in data-driven computational science are considered. The main problem of interest is the multiobjective optimization problem, where the tradeoff surface (called the Pareto front) between multiple conflicting objectives must be approximated in order to identify designs that balance real-world tradeoffs. In order to solve multiobjective optimization problems that are derived from computationally expensive blackbox functions, such as engineering design optimization problems, several methodologies are combined, including surrogate modeling, trust region method
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ROSSI, FILIPPO. "Mathematical models for selling process optimization." Doctoral thesis, Università degli studi di Genova, 2021. http://hdl.handle.net/11567/1050078.

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The work of the Thesis has been pursued in collaboration with an important company operating in the tourist sector. The followed projects in the work can be seen as belonging to the Destination Management branch, that is the study and the implementation of actions aimed at better managing the company offer related to touristic experiences broadly. In particular, the first project has been related to Revenue Forecasting and has dealt with the definition of a methodology, based on mathematical and statistical techniques, aimed at forecasting the revenue streams linked to specific items of a com
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Rossetti, Gaia. "Mathematical optimization techniques for cognitive radar networks." Thesis, Loughborough University, 2018. https://dspace.lboro.ac.uk/2134/33419.

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This thesis discusses mathematical optimization techniques for waveform design in cognitive radars. These techniques have been designed with an increasing level of sophistication, starting from a bistatic model (i.e. two transmitters and a single receiver) and ending with a cognitive network (i.e. multiple transmitting and multiple receiving radars). The environment under investigation always features strong signal-dependent clutter and noise. All algorithms are based on an iterative waveform-filter optimization. The waveform optimization is based on convex optimization techniques and the expl
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Trescher, Saskia. "Estimating Gene Regulatory Activity using Mathematical Optimization." Doctoral thesis, Humboldt-Universität zu Berlin, 2020. http://dx.doi.org/10.18452/21900.

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Die Regulation der Genexpression ist einer der wichtigsten zellulären Prozesse und steht in Zusammenhang mit der Entstehung diverser Krankheiten. Regulationsmechanismen können mit einer Vielzahl von Methoden experimentell untersucht werden, zugleich erfordert die Integration der Datensätze in umfassende Modelle stringente rechnergestützte Methoden. Ein Teil dieser Methoden modelliert die genomweite Genexpression als (lineares) Gleichungssystem über die Aktivität und Beziehungen von Transkriptionsfaktoren (TF), Genen und anderen Faktoren und optimiert die Parameter, sodass die gemessenen Expres
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Haddon, Antoine. "Mathematical Modeling and Optimization for Biogas Production." Thesis, Montpellier, 2019. http://www.theses.fr/2019MONTS047.

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La digestion anaérobique est un processus biologique au cours duquel des micro-organismes décomposent de la matière organique pour produire du biogaz (dioxyde de carbone et methane) qui peut être utilisé comme source d'énergie renouvelable. Cette thèse porte sur l'élaboration de stratégies de contrôle et la conception de bioréacteurs qui maximisent la production de biogaz.La première partie se concentre sur le problème de contrôle optimal de la maximisation de la production de biogaz dans un chemostat avec un modèle à une réaction, en contrôlant le taux de dilution. Pour le problème à horizon
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Books on the topic "Mathematical optimization"

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Snyman, Jan A., and Daniel N. Wilke. Practical Mathematical Optimization. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77586-9.

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L, Rardin Ronald, ed. Discrete optimization. Academic Press, 1988.

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Dingzhu, Du, Pardalos P. M. 1954-, and Wu Weili, eds. Mathematical theory of optimization. Kluwer Academic, 2001.

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Hoffmann, Karl-Heinz, Jochem Zowe, Jean-Baptiste Hiriart-Urruty, and Claude Lemarechal, eds. Trends in Mathematical Optimization. Birkhäuser Basel, 1988. http://dx.doi.org/10.1007/978-3-0348-9297-1.

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Pallaschke, Diethard, and Stefan Rolewicz. Foundations of Mathematical Optimization. Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-017-1588-1.

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Hürlimann, Tony. Mathematical Modeling and Optimization. Springer US, 1999. http://dx.doi.org/10.1007/978-1-4757-5793-4.

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Du, Ding-Zhu, Panos M. Pardalos, and Weili Wu, eds. Mathematical Theory of Optimization. Springer US, 2001. http://dx.doi.org/10.1007/978-1-4757-5795-8.

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Oberwolfach), Tagung Methoden und Verfahren der Mathematischen Physik (11th 1985 Mathematisches Forschungsinstitut. Optimization in mathematical physics. P. Lang, 1987.

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Du, Dingzhu. Mathematical Theory of Optimization. Springer US, 2001.

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Guddat, Jürgen. Multiobjective and stochastic optimization based on parametric optimization. Akademie-Verlag, 1985.

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Book chapters on the topic "Mathematical optimization"

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Schittkowski, Klaus. "Mathematical Optimization." In Software Systems for Structural Optimization. Birkhäuser Basel, 1993. http://dx.doi.org/10.1007/978-3-0348-8553-9_2.

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Wang, Liang, and Jianxin Zhao. "Mathematical Optimization." In Architecture of Advanced Numerical Analysis Systems. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8853-5_4.

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Pappalardo, Elisa, Panos M. Pardalos, and Giovanni Stracquadanio. "Mathematical Optimization." In SpringerBriefs in Optimization. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-9053-1_3.

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Cao, Bing-Yuan. "Mathematical Preliminaries." In Applied Optimization. Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0009-4_1.

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Kogan, Konstantin, and Eugene Khmelnitsky. "Mathematical Background." In Applied Optimization. Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4675-7_2.

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Schittkowski, Klaus. "Mathematical Foundations." In Applied Optimization. Springer US, 2002. http://dx.doi.org/10.1007/978-1-4419-5762-7_2.

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Belenky, Alexander S. "Mathematical Programming." In Applied Optimization. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4757-6075-0_2.

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Lobato, Fran Sérgio, and Valder Steffen. "Mathematical." In Multi-Objective Optimization Problems. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58565-9_5.

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Neumaier, Arnold. "Mathematical Model Building." In Applied Optimization. Springer US, 2004. http://dx.doi.org/10.1007/978-1-4613-0215-5_3.

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Bhatti, M. Asghar. "Mathematical Preliminaries." In Practical Optimization Methods. Springer New York, 2000. http://dx.doi.org/10.1007/978-1-4612-0501-2_3.

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Conference papers on the topic "Mathematical optimization"

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Chen, Guoxin, Minpeng Liao, Chengxi Li, and Kai Fan. "Step-level Value Preference Optimization for Mathematical Reasoning." In Findings of the Association for Computational Linguistics: EMNLP 2024. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-emnlp.463.

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Zhao, Xueliang, Xinting Huang, Wei Bi, and Lingpeng Kong. "SEGO: Sequential Subgoal Optimization for Mathematical Problem-Solving." In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.acl-long.407.

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Santhamoorthy, Pooja Zen, and Selen Cremaschi. "Mathematical Optimization of Separator Network Design for Sand Management." In Foundations of Computer-Aided Process Design. PSE Press, 2024. http://dx.doi.org/10.69997/sct.154881.

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Sand produced along with well-production fluids accumulates in the surface facilities over time, taking valuable space, while the sand carried with the fluids damages downstream equipment. Thus, sand is separated from the fluid in the sand traps and separators and removed during periodic clean-ups. But at high sand productions, the probability of unscheduled facilities shutdowns increases. Such extreme production conditions can be handled by strategic planning and optimal design of the separator network to enable maximum sand separation at minimal equipment cost while ensuring the accumulation
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De Kock, D. J., M. Nagulapally, J. A. Visser, R. Nair, and J. Nigen. "Mathematical Optimization of Electronic Enclosures." In ASME 2005 Pacific Rim Technical Conference and Exhibition on Integration and Packaging of MEMS, NEMS, and Electronic Systems collocated with the ASME 2005 Heat Transfer Summer Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/ipack2005-73185.

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The thermal design of electronic enclosures is becoming more important as the demand for smaller, lighter systems with better performance increases. The limiting factor on the lifetime of these systems is the maximum temperature of the electronic components. Nowadays in some systems, the thermal design is the limiting factor for performance increases. A simple yet effective design method that yields optimum designs is therefore required to design these systems. Traditionally, experimental methods were used in the design of electronic enclosures. More recently Computational Fluid Dynamics (CFD)
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Findeisen, Bernd, Mario Schwalbe, Norman Gunther, and Lutz Stiegler. "NVH Optimization of Driveline with Mathematical Optimization Methods." In Symposium on International Automotive Technology 2013. SAE International, 2013. http://dx.doi.org/10.4271/2013-26-0089.

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Poole, Daniel J., Christian B. Allen, and T. Rendall. "Metric-Based Mathematical Derivation of Aerofoil Design Variables." In 10th AIAA Multidisciplinary Design Optimization Conference. American Institute of Aeronautics and Astronautics, 2014. http://dx.doi.org/10.2514/6.2014-0114.

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Morris, R. M., J. A. Snyman, and Josua P. Meyer. "MATHEMATICAL OPTIMIZATION OF JETS IN CROSSFLOW." In Annals of the Assembly for International Heat Transfer Conference 13. Begell House Inc., 2006. http://dx.doi.org/10.1615/ihtc13.p26.200.

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EWING, M., and V. VENKAYYA. "Structural identification using mathematical optimization techniques." In 32nd Structures, Structural Dynamics, and Materials Conference. American Institute of Aeronautics and Astronautics, 1991. http://dx.doi.org/10.2514/6.1991-1135.

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Almosa, Nadia Ali Abbas, and Ahmed Sabah Al-Jilawi. "Developing mathematical optimization models with Python." In AL-KADHUM 2ND INTERNATIONAL CONFERENCE ON MODERN APPLICATIONS OF INFORMATION AND COMMUNICATION TECHNOLOGY. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0119585.

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Lee, Eva K., Tsung-Lin Wu, Onur Seref, O. Erhun Kundakcioglu, and Panos Pardalos. "Classification and disease prediction via mathematical programming." In DATA MINING, SYSTEMS ANALYSIS AND OPTIMIZATION IN BIOMEDICINE. AIP, 2007. http://dx.doi.org/10.1063/1.2817343.

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Reports on the topic "Mathematical optimization"

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Lovianova, Iryna V., Dmytro Ye Bobyliev, and Aleksandr D. Uchitel. Cloud calculations within the optional course Optimization Problems for 10th-11th graders. [б. в.], 2019. http://dx.doi.org/10.31812/123456789/3267.

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The article deals with the problem of introducing cloud calculations into 10th-11th graders’ training to solve optimization problems in the context of the STEM-education concept. After analyzing existing programmes of optional courses on optimization problems, the programme of the optional course Optimization Problems has been developed and substantiated implying solution of problems by the cloud environment CoCalc. It is a routine calculating operation and not a mathematical model that is accentuated in the programme. It allows considering more problems which are close to reality without adap
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Venkayya, Vipperla B., and Victoria A. Tischler. A Compound Scaling Algorithm for Mathematical Optimization. Defense Technical Information Center, 1989. http://dx.doi.org/10.21236/ada208446.

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Eskow, Elizabeth, and Robert B. Schnabel. Mathematical Modeling of a Parallel Global Optimization Algorithm. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada446514.

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Pasupuleti, Murali Krishna. Mathematical Modeling for Machine Learning: Theory, Simulation, and Scientific Computing. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv125.

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Abstract Mathematical modeling serves as a fundamental framework for advancing machine learning (ML) and artificial intelligence (AI) by integrating theoretical, computational, and simulation-based approaches. This research explores how numerical optimization, differential equations, variational inference, and scientific computing contribute to the development of scalable, interpretable, and efficient AI systems. Key topics include convex and non-convex optimization, physics-informed machine learning (PIML), partial differential equation (PDE)-constrained AI, and Bayesian modeling for uncertai
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De Silva, K. N. A mathematical model for optimization of sample geometry for radiation measurements. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1988. http://dx.doi.org/10.4095/122732.

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Xie, Haiyan, and Mangolika Bhattacharya. MATH-DT: Development of Mathematical Models for Large-Scale Nonlinear Optimization for Digital Twins Nomenclature Table. Illinois State University, 2025. https://doi.org/10.30707/1741890724.245225.

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The nomenclature table for the Mathematical Digital Twin project description systematically defines key variables, parameters, and mathematical symbols used throughout the research. The table is deposited for open access, allowing other researchers to reference and build upon the work to enhance transparency and reproducibility.
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Wegley, H. L., and J. C. Barnard. Using the NOABL flow model and mathematical optimization as a micrositing tool. Office of Scientific and Technical Information (OSTI), 1986. http://dx.doi.org/10.2172/6979883.

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Turinsky, Paul, and Ross Hays. Development and Utilization of mathematical Optimization in Advanced Fuel Cycle Systems Analysis. Office of Scientific and Technical Information (OSTI), 2011. http://dx.doi.org/10.2172/1024390.

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Iyer, Ananth V., Samuel Labi, Steven R. Dunlop, et al. Heavy Fleet and Facilities Optimization. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317365.

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The Indiana Department of Transportation (INDOT) is responsible for timely clearance of snow on state-maintained highways in Indiana as part of its wintertime operations. For this and other maintenance purposes, the state’s subdistricts maintain 101 administrative units spread throughout the state. These units are staffed by personnel, including snow truck drivers and house snow removal trucks and other equipment. INDOT indicated a need to carry out value engineering analysis of the replacement timing of the truck fleet. To address these questions, this study carried out analysis to ascertain
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Hector Colonmer, Prabhu Ganesan, Nalini Subramanian, Dr. Bala Haran, Dr. Ralph E. White, and Dr. Branko N. Popov. OPTIMIZATION OF THE CATHODE LONG-TERM STABILITY IN MOLTEN CARBONATE FUEL CELLS: EXPERIMENTAL STUDY AND MATHEMATICAL MODELING. Office of Scientific and Technical Information (OSTI), 2002. http://dx.doi.org/10.2172/808855.

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