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              <text>Effective Techniques Non-linear Dynamic Model Calibration using CNN</text>
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              <text>Calibration; Convolutional Neural Network; Mapping; Nonlinear Dynamic</text>
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              <text>This paper proposes an efficient method to estimate nonlinear dynamic models using convolutional neural networks (CNNs). The proposed method combines the power of statistical optimization and machine learning to obtain more accurate and efficient estimates of complex models by training CNNs to recognize maps featuring input models and between results, thereby reducing the computational cost of measurements and then using the trained CNN to generate surrogate models -The method can determine accuracy for a range of exposed cases in various nonlinear dynamic models, including differential equation model of chemical reactor and stochastic model of biological systems The results show that the proposed methods are effective for measuring these models, if at most with such accuracy and reducing the computational cost in terms of both frequency and magnitude, the proposed method represents a promising method for estimating nonlinear dynamic models, offering significant advantages in terms of accuracy, efficiency and in scalability   2024 IEEE.</text>
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              <text>Devanathan M.; Prasannan D.; Singh P.; Joshua O.; Adity Pai H.; Jakhar P.</text>
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              <text>Proceedings - International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024, pp. 832-836.</text>
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              <text>Institute of Electrical and Electronics Engineers Inc.</text>
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              <text>&lt;a href="https://doi.org/10.1109/IC2PCT60090.2024.10486219" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/IC2PCT60090.2024.10486219&lt;/a&gt;
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              <text>ISBN: 979-835038352-2</text>
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              <text>Devanathan M., Christ University, Karnataka, Bengaluru, India; Prasannan D., Karpagam College of Engineering, Department of Civil Engineering, Coimbatore, India; Singh P., Sgt University, Faculty of Science, Department of Physics, Gurugram, India; Joshua O., Afe Babalola University Ado-Ekiti, Civil and Environmental Engineering, Ekiti, Nigeria; Adity Pai H., Graphic Era Deemed to Be University, Department of Computer Science &amp;amp; Engineering, Uttarakhand, Dehradun, India; Jakhar P., Indian Institute of Technology Jodhpur, Rajasthan, Jodhpur, India</text>
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