jekim5418/DPM
Official code for DPM : A Novel Training Method for Physics-Informed Neural Networks in Extrapolation
This project helps scientists and engineers accurately predict how physical systems behave beyond the conditions they were initially trained on. It takes equations describing physical processes (like fluid dynamics or quantum mechanics) and uses a new method to train neural networks. The output is a more reliable model that can extrapolate solutions for these complex partial differential equations, especially when traditional methods struggle to predict future states or untested conditions.
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Use this if you need to model complex physical phenomena governed by Partial Differential Equations and require highly accurate predictions for conditions outside your initial training data.
Not ideal if your modeling needs are purely interpolative or if you are not working with physics-informed neural networks.
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Language
Python
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Last pushed
Nov 02, 2021
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