Deep Learning Methods Of Mathematical Physics - Volume I: Direct And Inverse Problems
by Ovidiu Calin
Deep Learning Methods of Mathematical Physics - Volume I examines the application of deep learning to direct and inverse problems in mathematical physics. Authored by Ovidiu Calin, it offers essential techniques for leveraging neural networks in solving complex physical models and equations, advancing computational approaches in the field.
About This Book
This volume introduces deep learning methods tailored for mathematical physics, focusing on direct and inverse problems. It provides a structured approach to applying neural networks in solving complex equations and modeling physical phenomena.
Readers will find detailed explorations of how these methods enhance traditional techniques in physics, offering insights into optimization and computational efficiency. The content is designed for researchers and students seeking innovative tools in applied mathematics.
With a emphasis on practical implementations, the book serves as a foundational resource for integrating machine learning with physical sciences, addressing key challenges in simulation and prediction.
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