Relevant bibliographies by topics / Computational Intelligence Methods

Contents

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

Academic literature on the topic 'Computational Intelligence Methods'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Computational Intelligence Methods"

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ANTONIOU, GRIGORIS, and NEIL V. MURRAY. "Logical methods for computational intelligence." Knowledge Engineering Review 12, no. 4 (1997): 407–9. http://dx.doi.org/10.1017/s0269888997003056.

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Over the past years, two main approaches to computational intelligence have emerged: the symbolic and the non-symbolic approach. The perhaps most prominent methods of the symbolic approach are based on logic. Logical methods exhibit a series of desirable properties:[bull ] Transparent representation of meaning[bull ] Precise understanding of the meaning of statements (semantics).[bull ] Sound reasoning methods.[bull ] Explanation capabilities.A special session on logical methods for computational intelligence was held at the 3rd Joint Conference on Information Sciences. The field of computatio
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Antoniou, Grigoris, and Neil V. Murray. "Logical methods for computational intelligence." Information Sciences 116, no. 1 (1999): 1–2. http://dx.doi.org/10.1016/s0020-0255(98)10091-9.

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Hecht, David, and Gary Fogel. "Computational Intelligence Methods for Docking Scores." Current Computer Aided-Drug Design 5, no. 1 (2009): 56–68. http://dx.doi.org/10.2174/157340909787580863.

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Cass, R., and J. DePietro. "Computational intelligence methods for process discovery." Engineering Applications of Artificial Intelligence 11, no. 5 (1998): 675–81. http://dx.doi.org/10.1016/s0952-1976(98)00033-5.

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Jorge, Belinha. "Artificial Intelligence in Computational Mechanics and Biomechanics." Journal of Computation and Artificial Intelligence in Mechanics and Biomechanics 1, no. 1 (2021): 1–6. https://doi.org/10.5281/zenodo.4669522.

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<em>This work aims to deliver a brief presentation and evolution of Artificial intelligence (AI) and its potentially most suited methodologies for Computational Mechanics and Biomechanics (CM&amp;B) applications, such as Machine Learning (ML), Pattern Recognition (PR), Deep Learning (DL) and DL using artificial Neural Networks (NN). Afterwards, since DL using artificial NN is the AI methodology mostly used in CM&amp;B, this methodology applied to CM&amp;B problems is described with more detail. In order to show the evolution of AI methodologies in CM&amp;B, a large document search was performe
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Ilin, Vladimir, and Dragan Simić. "A review of computational intelligence methods for traffic management systems." Put i saobraćaj 67, no. 4 (2021): 25–30. http://dx.doi.org/10.31075/67.04.05.

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One of the most important challenges in modern city life is to enable effective and efficient traffic management system. Recently, computational intelligence methods have become increasingly popular for traffic management system design, application, and monitoring. Computational intelligence methods are often deployed for managing traffic, that is for reducing mileage, congestion, the use of fuels, and environmental impact. The aim of this paper is twofold. First, to present the three main areas in a computational intelligence approach, namely neural networks, fuzzy logic systems, and evolutio
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Ilin, Vladimir, and Dragan Simić. "A review of computational intelligence methods for traffic management systems." Put i saobraćaj 67, no. 4 (2021): 25–30. http://dx.doi.org/10.31075/pis.67.04.05.

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One of the most important challenges in modern city life is to enable effective and efficient traffic management system. Recently, computational intelligence methods have become increasingly popular for traffic management system design, application, and monitoring. Computational intelligence methods are often deployed for managing traffic, that is for reducing mileage, congestion, the use of fuels, and environmental impact. The aim of this paper is twofold. First, to present the three main areas in a computational intelligence approach, namely neural networks, fuzzy logic systems, and evolutio
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Żabiński, T., G. Piecuch, R. Żyła, and S. Prucnal. "Milling process diagnosis using computational intelligence methods." IOP Conference Series: Materials Science and Engineering 710 (December 19, 2019): 012043. http://dx.doi.org/10.1088/1757-899x/710/1/012043.

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Kruse, R., and M. Steinbrecher. "Visual data analysis with computational intelligence methods." Bulletin of the Polish Academy of Sciences: Technical Sciences 58, no. 3 (2010): 393–401. http://dx.doi.org/10.2478/v10175-010-0037-z.

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Visual data analysis with computational intelligence methodsVisual data analysis is an appealing and increasing field of application. We present two related visual analysis approaches that allow for the visualization of graphical model parameters and time-dependent association rules. When the graphical model is defined over purely nominal attributes, its local structure can be interpreted as an association rule. Such association rules comprise one of the most prominent and wide-spread analysis techniques for pattern detection, however, there are only few visualization methods. We introduce an
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PATEL, PRETESH B., and TSHILIDZI MARWALA. "CALLER BEHAVIOUR CLASSIFICATION USING COMPUTATIONAL INTELLIGENCE METHODS." International Journal of Neural Systems 20, no. 01 (2010): 87–93. http://dx.doi.org/10.1142/s0129065710002255.

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A classification system that accurately categorizes caller interaction within Interactive Voice Response systems is essential in determining caller behaviour. Field and call performance classifier for pay beneficiary application are developed. Genetic Algorithms, Multi-Layer Perceptron neural network, Radial Basis Function neural network, Fuzzy Inference Systems and Support Vector Machine computational intelligent techniques were considered in this research. Exceptional results were achieved. Classifiers with accuracy values greater than 90% were developed. The preferred models for field 'Say
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Dissertations / Theses on the topic "Computational Intelligence Methods"

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Kotta, Anwesh. "Condition Monitoring : Using Computational intelligence methods." Master's thesis, Universitätsbibliothek Chemnitz, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-187100.

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Machine tool components are widely used in many industrial applications. In accordance with their usage, a reliable health monitoring system is necessary to detect defects in these components in order monitor machinery performance and avoid malfunction. Even though several techniques have been reported for fault detection and diagnosis, it is a challenging task to implement a condition monitoring system in real world applications due to their complexity in structure and noisy operating environment. The primary objective of this thesis is to develop novel intelligent algorithms for a reliable f
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Eminoglu, Ilyas. "Computational intelligence methods : generic interpretations, optimisation and application." Thesis, University of Sheffield, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.289703.

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Ekong, Udeme Essien. "Development of computational intelligence methods to deal with classification problems." Thesis, King's College London (University of London), 2016. https://kclpure.kcl.ac.uk/portal/en/theses/development-of-computational-intelligence-methods-to-deal-with-classi-cation-problems(0f04e932-0e3d-4309-a21f-1e2f6df6d29d).html.

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In the thesis presented here, variations of two very prominent machine learning techniques, the Neural Network (NN) and Support Vector Machine (SVM) are used in an attempt to solve two classification problems. Classification involves the assignment of an unknown object into a pre-determined group which consists of a set of preclassified objects with similar features to that unknown object. The main theme of the research conducted in this thesis involves investigation into existing and proposed classifier architectures to improve the classification performance for certain research problems. The
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West, Graeme Michael. "Computational intelligence methods for power system protection design and decision support." Thesis, University of Strathclyde, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.400311.

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Green, Robert C. II. "Novel Computational Methods for the Reliability Evaluation of Composite Power Systems using Computational Intelligence and High Performance Computing Techniques." University of Toledo / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1338894641.

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Devergne, Timothée. "Machine learning methods for computational studies in origins of life." Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS376.

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La chimie prébiotique consiste en l’étude des réactions chimiques aux origines de la vie sur Terre. C’est un très vaste sujet qui mobilise plusieurs domaines scientifiques dont la physique numérique. En effet, des simulations de dynamiques moléculaire de haute précision peuvent être menées pour tester l’influence de différents environnements plausibles sur la synthèse de molécules : ce composant a-t-il pu apparaître dans le milieu interstellaire ? Sa formation est-elle favorisée par la présence de surfaces minérales ? Elles peuvent aussi être utilisées pour identifier des intermédiaires réacti
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Abdullah, Rudwan Ali Abolgasim. "Intelligent methods for complex systems control engineering." Thesis, University of Stirling, 2007. http://hdl.handle.net/1893/257.

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This thesis proposes an intelligent multiple-controller framework for complex systems that incorporates a fuzzy logic based switching and tuning supervisor along with a neural network based generalized learning model (GLM). The framework is designed for adaptive control of both Single-Input Single-Output (SISO) and Multi-Input Multi-Output (MIMO) complex systems. The proposed methodology provides the designer with an automated choice of using either: a conventional Proportional-Integral-Derivative (PID) controller, or a PID structure based (simultaneous) Pole and Zero Placement controller. The
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JIMENEZ, JUAN DARIO TIMARAN. "METHODS BASED ON COMPUTATIONAL INTELLIGENCE TECHNIQUES FOR LOCALIZATION OF FIREARMS PROJECTILES INSERTED INTO THE HUMAN BODY, BY HIGH SENSITIVITY MAGNETIC MEASUREMENTS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2017. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=32028@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>PROGRAMA DE EXCELENCIA ACADEMICA<br>Um dos casos clínicos mais frequentes na sociedade moderna envolve a localização e extração de projéteis de armas de fogo, que normalmente são feitos de chumbo, um material não ferromagnético. O desenvolvimento de uma técnica que possibilite a localização precisa destes auxiliará o procedimento de remoção cirúrgica, tendo vasta relevância e impactando diretamente no aumento da taxa de sobrevivência de pessoas feridas. Dessa forma, esta disser
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Groves, Adrian R. "Bayesian learning methods for modelling functional MRI." Thesis, University of Oxford, 2009. http://ora.ox.ac.uk/objects/uuid:fe46e696-a1a6-4a9d-9dfe-861b05b1ed33.

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Bayesian learning methods are the basis of many powerful analysis techniques in neuroimaging, permitting probabilistic inference on hierarchical, generative models of data. This thesis primarily develops Bayesian analysis techniques for magnetic resonance imaging (MRI), which is a noninvasive neuroimaging tool for probing function, perfusion, and structure in the human brain. The first part of this work fits nonlinear biophysical models to multimodal functional MRI data within a variational Bayes framework. Simultaneously-acquired multimodal data contains mixtures of different signals and ther
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Ferreira, Junior Valnir, and N/A. "Improvements to Clause Weighting Local Search for Propositional Satisfiability." Griffith University. Institute for Integrated and Intelligent Systems, 2007. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20070823.123257.

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The propositional satisfiability (SAT) problem is of considerable theoretical and practical relevance to the artificial intelligence (AI) community and has been used to model many pervasive AI tasks such as default reasoning, diagnosis, planning, image interpretation, and constraint satisfaction. Computational methods for SAT have historically fallen into two broad categories: complete search and local search. Within the local search category, clause weighting methods are amongst the best alternatives for SAT, becoming particularly attractive on problems where a complete search is impractical
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Books on the topic "Computational Intelligence Methods"

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Koziel, Slawomir. Computational Optimization, Methods and Algorithms. Springer Berlin Heidelberg, 2011.

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Silhavy, Radek, Petr Silhavy, and Zdenka Prokopova, eds. Applied Computational Intelligence and Mathematical Methods. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-67621-0.

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Nguyen, Ngoc Thanh, Bogdan Trawiński, Radosław Katarzyniak, and Geun-Sik Jo, eds. Advanced Methods for Computational Collective Intelligence. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34300-1.

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Silhavy, Radek, ed. Artificial Intelligence and Bioinspired Computational Methods. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51971-1.

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Marwala, Tshilidzi. Condition Monitoring Using Computational Intelligence Methods. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2380-4.

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Masulli, Francesco, Leif E. Peterson, and Roberto Tagliaferri, eds. Computational Intelligence Methods for Bioinformatics and Biostatistics. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14571-1.

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Klempous, Ryszard, Jan Nikodem, Witold Jacak, and Zenon Chaczko, eds. Advanced Methods and Applications in Computational Intelligence. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-01436-4.

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Bracciali, Andrea, Giulio Caravagna, David Gilbert, and Roberto Tagliaferri, eds. Computational Intelligence Methods for Bioinformatics and Biostatistics. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67834-4.

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Rizzo, Riccardo, and Paulo J. G. Lisboa, eds. Computational Intelligence Methods for Bioinformatics and Biostatistics. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21946-7.

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Raposo, Maria, Paulo Ribeiro, Susana Sério, Antonino Staiano, and Angelo Ciaramella, eds. Computational Intelligence Methods for Bioinformatics and Biostatistics. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-34585-3.

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Book chapters on the topic "Computational Intelligence Methods"

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Adjailia, Fouzia, and Michal Takáč. "Computational Methods in Computational Fluid Dynamics." In Artificial Intelligence and Sustainable Computing. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1431-9_7.

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Kozak, Jan. "Ensemble Methods." In Studies in Computational Intelligence. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93752-6_6.

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Zgurovsky, Michael, Victor Sineglazov, and Elena Chumachenko. "Intelligence Methods of Forecasting." In Studies in Computational Intelligence. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48453-8_5.

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Peng, Hong, and Jun Wang. "Computational Completeness." In Computational Intelligence Methods and Applications. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-5280-5_3.

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Byrski, Aleksander, and Marek Kisiel-Dorohinicki. "Contemporary Methods of Computational Intelligence." In Evolutionary Multi-Agent Systems. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-51388-1_1.

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Ansari, Nirwan, and Edwin Hou. "Heuristic Search Methods." In Computational Intelligence for Optimization. Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6331-0_2.

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da Cruz, André Vargas Abs, Carlos Hall Barbosa, Juan Guillermo Lazo Lazo, et al. "Decision Support Methods." In Studies in Computational Intelligence. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-93000-6_3.

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Maeda, Ken-ichi. "From the Subspace Methods to the Mutual Subspace Method." In Studies in Computational Intelligence. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12848-6_5.

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Yong, Longquan. "Nonlinear Complementarity Problem and Solution Methods." In Artificial Intelligence and Computational Intelligence. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16530-6_55.

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Signoretto, Marco, and Johan A. K. Suykens. "Kernel Methods." In Springer Handbook of Computational Intelligence. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-43505-2_32.

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Conference papers on the topic "Computational Intelligence Methods"

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Ihesiulor, Obinna K., Krishna Shankar, Zhifang Zhang, and Tapabrata Ray. "Delamination detection using methods of computational intelligence." In PROCEEDINGS OF THE SIXTH GLOBAL CONFERENCE ON POWER CONTROL AND OPTIMIZATION. AIP, 2012. http://dx.doi.org/10.1063/1.4769006.

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Valdes, Julio J., Catherine Cheung, and Weichao Wang. "Computational intelligence methods for helicopter loads estimation." In 2011 International Joint Conference on Neural Networks (IJCNN 2011 - San Jose). IEEE, 2011. http://dx.doi.org/10.1109/ijcnn.2011.6033451.

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Mohamed-Seghir, Mostefa. "Computational intelligence method for ship trajectory planning." In 2016 21st International Conference on Methods and Models in Automation and Robotics (MMAR). IEEE, 2016. http://dx.doi.org/10.1109/mmar.2016.7575210.

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Norton, Samuel, Taylor Ramsay, Kostas Karatzas, Jens Fridh, and Paul Petrie-Repar. "Modelling Of Turbine Blade Vibrations Via Computational Intelligence Methods." In European Conference on Turbomachinery Fluid Dynamics and hermodynamics. European Turbomachinery Society, 2017. http://dx.doi.org/10.29008/etc2017-083.

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Balas, Valentina E. "Advanced Computational Intelligence Methods and Practical Applications: PLENARY PAPER." In 2019 IEEE 19th International Symposium on Computational Intelligence and Informatics and 7th IEEE International Conference on Recent Achievements in Mechatronics, Automation, Computer Sciences and Robotics (CINTI-MACRo). IEEE, 2019. http://dx.doi.org/10.1109/cinti-macro49179.2019.9105295.

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Tettey, T., and T. Marwala. "Conflict Modelling and Knowledge Extraction using Computational Intelligence Methods." In Intelligent Engineering Systems, 2007 International Conference on. IEEE, 2007. http://dx.doi.org/10.1109/ines.2007.4283691.

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Ferreira, P. M., and A. E. Ruano. "Application of computational intelligence methods to greenhouse environmental modelling." In 2008 IEEE International Joint Conference on Neural Networks (IJCNN 2008 - Hong Kong). IEEE, 2008. http://dx.doi.org/10.1109/ijcnn.2008.4634310.

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Gladkov, Leonid A., Nadezhda V. Gladkova, and Sergey N. Leyba. "Solving Design Optimization Problems Based on Computational Intelligence Methods." In 2019 International Russian Automation Conference. IEEE, 2019. http://dx.doi.org/10.1109/rusautocon.2019.8867776.

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Jurado, Sergio, Juan Peralta, Angela Nebot, Francisco Mugica, and Paulo Cortez. "Short-term electric load forecasting using computational intelligence methods." In 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2013. http://dx.doi.org/10.1109/fuzz-ieee.2013.6622523.

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Decherchi, Sergio, Davide Leoncini, Paolo Gastaldo, Rodolfo Zunino, Osvaldo Faggioni, and Maurizio Soldani. "Computational intelligence methods for underwater magnetic-based protection systems." In 2011 International Joint Conference on Neural Networks (IJCNN 2011 - San Jose). IEEE, 2011. http://dx.doi.org/10.1109/ijcnn.2011.6033227.

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Reports on the topic "Computational Intelligence Methods"

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Danylchuk, Hanna B., and Serhiy O. Semerikov. Advances in machine learning for the innovation economy: in the shadow of war. Криворізький державний педагогічний університет, 2023. http://dx.doi.org/10.31812/123456789/7732.

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This preface introduces the selected and revised papers presented at the 10th International Conference on Monitoring, Modeling &amp; Management of Emergent Economy (M3E2 2022), held online in Ukraine, on November 17-18, 2022. The conference aimed to bring together researchers, practitioners, and students from various fields to exchange ideas, share experiences, and discuss challenges and opportunities in applying computational intelligence and data science for the innovation economy. The innovation economy is a term that describes the emerging paradigm of economic development that is driven by
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Liu, Tong, and Hadi Meidani. Artificial Intelligence for Optimal Truck Platooning: Impact on Autonomous Freight Delivery. Illinois Center for Transportation, 2023. http://dx.doi.org/10.36501/0197-9191/23-017.

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The advancements in autonomous- and connected-vehicle technologies bring drastic changes in freight delivery. Vehicle-to-vehicle and vehicle-to-infrastructure communication has become a reality with the help of autonomous and connected vehicles. One of the most notable changes is the formation of truck platoons. Despite the numerous benefits of truck platooning, such as reduced fuel consumption and increased traffic efficiency, this approach requires a significant amount of computational resources to obtain aerodynamic performance under different scenarios. To overcome this challenge, a data-d
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Pasupuleti, Murali Krishna. Stochastic Computation for AI: Bayesian Inference, Uncertainty, and Optimization. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv325.

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Abstract: Stochastic computation is a fundamental approach in artificial intelligence (AI) that enables probabilistic reasoning, uncertainty quantification, and robust decision-making in complex environments. This research explores the theoretical foundations, computational techniques, and real-world applications of stochastic methods, focusing on Bayesian inference, Monte Carlo methods, stochastic optimization, and uncertainty-aware AI models. Key topics include probabilistic graphical models, Markov Chain Monte Carlo (MCMC), variational inference, stochastic gradient descent (SGD), and Bayes
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Pasupuleti, Murali Krishna. Quantum-Enhanced Machine Learning: Harnessing Quantum Computing for Next-Generation AI Systems. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv125.

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Abstract Quantum-enhanced machine learning (QML) represents a paradigm shift in artificial intelligence by integrating quantum computing principles to solve complex computational problems more efficiently than classical methods. By leveraging quantum superposition, entanglement, and parallelism, QML has the potential to accelerate deep learning training, optimize combinatorial problems, and enhance feature selection in high-dimensional spaces. This research explores foundational quantum computing concepts relevant to AI, including quantum circuits, variational quantum algorithms, and quantum k
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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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Pasupuleti, Murali Krishna. Optimal Control and Reinforcement Learning: Theory, Algorithms, and Robotics Applications. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv225.

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Abstract: Optimal control and reinforcement learning (RL) are foundational techniques for intelligent decision-making in robotics, automation, and AI-driven control systems. This research explores the theoretical principles, computational algorithms, and real-world applications of optimal control and reinforcement learning, emphasizing their convergence for scalable and adaptive robotic automation. Key topics include dynamic programming, Hamilton-Jacobi-Bellman (HJB) equations, policy optimization, model-based RL, actor-critic methods, and deep RL architectures. The study also examines traject
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Bailey Bond, Robert, Pu Ren, James Fong, Hao Sun, and Jerome F. Hajjar. Physics-informed Machine Learning Framework for Seismic Fragility Analysis of Steel Structures. Northeastern University, 2024. http://dx.doi.org/10.17760/d20680141.

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The seismic assessment of structures is a critical step to increase community resilience under earthquake hazards. This research aims to develop a Physics-reinforced Machine Learning (PrML) paradigm for metamodeling of nonlinear structures under seismic hazards using artificial intelligence. Structural metamodeling, a reduced-fidelity surrogate model to a more complex structural model, enables more efficient performance-based design and analysis, optimizing structural designs and ease the computational effort for reliability fragility analysis, leading to globally efficient designs while maint
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Muelaner, Jody Emlyn. Generative Design in Aerospace and Automotive Structures. SAE International, 2024. http://dx.doi.org/10.4271/epr2024016.

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&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Semi-automated computational design methods involving physics-based simulation, optimization, machine learning, and generative artificial intelligence (AI) already allow greatly enhanced performance alongside reduced cost in both design and manufacturing. As we progress, developments in user interfaces, AI integration, and automation of workflows will increasingly reduce the human inputs required to achieve this. With this, engineering teams must change their mindset from designing products to specifying requirements, fo
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BARKHATOV, NIKOLAY, and SERGEY REVUNOV. A software-computational neural network tool for predicting the electromagnetic state of the polar magnetosphere, taking into account the process that simulates its slow loading by the kinetic energy of the solar wind. SIB-Expertise, 2021. http://dx.doi.org/10.12731/er0519.07122021.

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The auroral activity indices AU, AL, AE, introduced into geophysics at the beginning of the space era, although they have certain drawbacks, are still widely used to monitor geomagnetic activity at high latitudes. The AU index reflects the intensity of the eastern electric jet, while the AL index is determined by the intensity of the western electric jet. There are many regression relationships linking the indices of magnetic activity with a wide range of phenomena observed in the Earth's magnetosphere and atmosphere. These relationships determine the importance of monitoring and predicting ge
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10

Pasupuleti, Murali Krishna. Neural Computation and Learning Theory: Expressivity, Dynamics, and Biologically Inspired AI. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv425.

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Abstract: Neural computation and learning theory provide the foundational principles for understanding how artificial and biological neural networks encode, process, and learn from data. This research explores expressivity, computational dynamics, and biologically inspired AI, focusing on theoretical expressivity limits, infinite-width neural networks, recurrent and spiking neural networks, attractor models, and synaptic plasticity. The study investigates mathematical models of function approximation, kernel methods, dynamical systems, and stability properties to assess the generalization capa
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