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De-noising and super-resolution of fluid-flow velocity measurements by optimising a discrete loss (ODIL)
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DOI：10.1016/j.ijheatfluidflow.2025.109988.png)
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Abstract
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• Both ODIL and PINN can improve accuracy and reduce noise. • ODIL converges faster than PINN for the same accuracy. • ODIL is prone to overfitting, so PINN achieves a higher accuracy. • Both methods are superior to statistical noise reduction approaches.
Keywords:
Machine Learning
Fluid Mechanics
Physics-informed neural networks
optimising a discrete loss
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