11 citations · 36 across the 10 of their papers we have counts for
10 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…
Minimal droplet shape representation in experimental microfluidics using Fourier series and autoencoders
Mihir Durve, Jean-Michel Tucny, Sibilla Orsini +6
We introduce a two-step, fully reversible process designed to project the outer shape of a generic droplet onto a lower-dimensional space. The initial step involves representing th…
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…
Droplet shape representation using Fourier series and autoencoders
Mihir Durve, Jean-Michel Tucny, Deepesh Bhamre +4
The shape of liquid droplets in air plays an important role in aerodynamic behavior and combustion dynamics of miniaturized propulsion systems such as microsatellites and small dro…
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…
Measuring arrangement and size distributions of flowing droplets in microchannels through deep learning
Mihir Durve, Sibilla Orsini, Adriano Tiribocchi +6
In microfluidic systems, droplets undergo intricate deformations as they traverse flow-focusing junctions, posing a challenging task for accurate measurement, especially during sho…
Learning of viscosity functions in rarefied gas flows with physics-informed neural networks
Jean-Michel Tucny, Mihir Durve, Andrea Montessori +1
The prediction non-equilibrium transport phenomena in disordered media is a difficult problem for conventional numerical methods. An example of a challenging problem is the predict…
Benchmarking YOLOv5 and YOLOv7 models with DeepSORT for droplet tracking applications
Mihir Durve, Sibilla Orsini, Adriano Tiribocchi +6
Tracking droplets in microfluidics is a challenging task. The difficulty arises in choosing a tool to analyze general microfluidic videos to infer physical quantities. The state-of…