qiauil/ConvDO
Convolutional Differential Operators for Physics-based Deep Learning Study
This project helps scientists and engineers working with physical simulations to accurately calculate spatial derivatives within 2D fields. It takes your physical system data and outputs highly precise derivative information, crucial for understanding how systems change over space, especially in applications like fluid dynamics or material science. It's designed for researchers and practitioners in physics-based deep learning.
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Use this if you need to compute high-order spatial derivatives for 2D physical simulations with various boundary conditions in a way that is compatible with deep learning frameworks.
Not ideal if you need to solve complex Partial Differential Equations (PDEs) directly or work with 3D fields, as it focuses specifically on derivative calculation in 2D.
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Language
Python
License
MIT
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Last pushed
Jul 30, 2024
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