AI4Science-WestlakeU/le-pde-uq
[AAAI24] LE-PDE-UQ endows deep learning-based surrogate models with robust and efficient uncertainty quantification capabilities for both forward and inverse problems.
This project helps scientists and engineers accurately quantify the uncertainty in their simulations involving physical systems, especially for long-term predictions. It takes in existing deep learning models trained on physical data governed by Partial Differential Equations (PDEs) and outputs a more reliable simulation with quantified uncertainty. This is useful for researchers in fields like physics, engineering, and climate science who rely on predictive models.
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Use this if you need to understand the reliability of your deep learning-based physical simulations, particularly for complex systems or long-term forecasting where uncertainty accumulates.
Not ideal if your primary goal is to simply speed up PDE solving without needing a rigorous measure of prediction uncertainty.
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16
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2
Language
Jupyter Notebook
License
MIT
Last pushed
Feb 26, 2024
Commits (30d)
0
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