MJfadeaway/DAS
DAS: A deep adaptive sampling method for solving high-dimensional partial differential equations
This helps researchers and engineers solve complex physics equations without needing extensive pre-calculated data. You input the structure of a high-dimensional partial differential equation, and it outputs a highly accurate numerical solution. It's designed for computational scientists and engineers working with challenging physical models.
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Use this if you need to find accurate solutions for complex, high-dimensional partial differential equations (PDEs) and want to avoid the sparsity problems of traditional uniform sampling methods.
Not ideal if your problems are low-dimensional or you prefer working with pre-labeled datasets for surrogate modeling, as a separate, PyTorch-based project handles that specific use case.
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Language
Python
License
MIT
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Last pushed
Nov 20, 2024
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