kaist-silab/awesome-graph-pde
Collection of resources about partial differential equations, graph neural networks, deep learning and dynamical system simulation
This collection helps researchers and practitioners explore cutting-edge methods for modeling and simulating complex systems. It compiles resources on how partial differential equations (PDEs), deep learning, and graph neural networks can be combined to understand dynamic systems. The target audience includes computational scientists, engineers, and applied mathematicians working on advanced simulations and data-driven modeling.
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Use this if you are a researcher or advanced practitioner interested in the intersection of physics-informed machine learning and graph neural networks for simulating dynamic systems.
Not ideal if you are looking for an off-the-shelf software tool for general graph data analysis or a basic introduction to deep learning.
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Jul 25, 2022
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