11 citations · 36 across the 10 of their papers we have counts for
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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…
Is the end of Insight in Sight ?
Jean-Michel Tucny, Mihir Durve, Sauro Succi
The rise of deep learning challenges the longstanding scientific ideal of insight - the human capacity to understand phenomena by uncovering underlying mechanisms. In many modern a…
Physics-Informed Neural Networks for microflows: Rarefied Gas Dynamics in Cylinder Arrays
Jean-Michel Tucny, Marco Lauricella, Mihir Durve +3
Accurate prediction of rarefied gas dynamics is crucial for optimizing flows through microelectromechanical systems, air filtration devices, and shale gas extraction. Traditional m…