4 papers
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…
Lattice Boltzmann simulations for soft flowing matter
A. Tiribocchi, M. Durve, M. Lauricella +3
Over the last decade, the Lattice Boltzmann method has found major scope for the simulation of a large spectrum of problems in soft matter, from multiphase and multi-component micr…