Explore the need for numerical methods in solving partial differential equations (PDEs), traditional techniques, neural networks' functioning, and the comparison between standard neural networks and physics-informed neural networks (PINN). Learn about the advantages, disadvantages of PINN, and ongoi

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Finite Element Methods (FEM) play a crucial role in solving complex PDEs in various domains. Overlapping Finite Elements in Julia aim to minimize reliance on mesh quality, improving solution accuracy. By leveraging Julia's matrix capabilities and efficient implementations, users can achieve faster c

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Professor Willi Jäger from the University of Heidelberg, Germany, is a renowned mathematician with a special focus on nonlinear systems, PDEs, and mathematical modeling. He has received numerous prestigious scientific awards and has made significant contributions to various fields of interest. Join

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Explore the world of Partial Differential Equations (PDEs) in the context of numerical methods. Learn about PDE classification, linear and nonlinear PDEs, notation, representing solutions, and applications like the heat equation. Dive into examples and concepts to enhance your understanding.

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This material delves into the properties, discretization methods, application in PDEs, grid considerations, linear equations solution, and more involved in Numerical Methods in Computational Fluid Dynamics. It covers approaches to fluid dynamical problems, components of numerical methods, and their

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