Interdisciplinary Applications of Traditional Numerical Methods, Deep Learning Methods, and Statistical Approaches
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Organizer(s): |
Name:
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Affiliation:
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Country:
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Qiaolin He
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Prof.
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Peoples Rep of China
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Xiaoling Peng
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Prof.
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Peoples Rep of China
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Introduction:
| | This session focuses on the combined application of traditional numerical methods, deep learning techniques, and statistical approaches to tackle real-world problems. Traditional numerical methods offer rigorous and precise solutions for complex mathematical models, while deep learning provides powerful tools for pattern recognition and prediction from large datasets. Statistical methods, on the other hand, enable effective data analysis and uncertainty quantification. By integrating these three approaches, we can leverage their respective strengths to address challenges in diverse fields such as engineering, finance, healthcare, and environmental science. This session will include case studies demonstrating successful integrations, discussions on overcoming integration challenges, and opportunities for collaborative problem-solving. Participants will gain insights into how this multidisciplinary approach can enhance our ability to solve practical problems more efficiently and accurately.
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