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cs.LG2025★ 1 cited
Randomness and signal propagation in physics-informed neural networks (PINNs): A neural PDE perspective
Jean-Michel Tucny, Abhisek Ganguly, Santosh Ansumali +1
Physics-informed neural networks (PINNs) often exhibit weight matrices that appear statistically random after training, yet their implications for signal propagation and stability…
cs.LG2024★ 11 cited
Can physical information aid the generalization ability of Neural Networks for hydraulic modeling?
Gianmarco Guglielmo, Andrea Montessori, Jean-Michel Tucny +2
Application of Neural Networks to river hydraulics is fledgling, despite the field suffering from data scarcity, a challenge for machine learning techniques. Consequently, many pur…