Olivier Pierre-Louis

Optimal Navigation in Stochastic and Disordered Gridworlds

Kévin Bilaï Biloa, Olivier Pierre-Louis

Navigation in complex and noisy environments is a key issue in diverse fields from biology to engineering. Despite extensive progress in numerical optimization methods for computing navigation policies, insights into how disorder reshapes optimal navigation remain elusive. To address this question, we …

Predictive patterning via solid-state dewetting of transferred single-crystal films

Seungjin Ju, Sangsun Lee, Donghwan Kim, Hyunsik Kim et autres

Abstract Designing and exploiting the dewetting of single-crystal films to create specific patterns for fabricating functional structures requires improved predictability and extensibility. In this study, we demonstrate that templated solid-state dewetting of single-crystal films can be guided to form regular patterns on …

kr, fr (code pays fourni par la source)

Learning lattice Kinetic Monte Carlo stochastic surface dynamics with Deep Generative Adversarial Networks

Daniele Lanzoni, Olivier Pierre-Louis, Roberto Bergamaschini, Francesco Montalenti

it, fr (code pays fourni par la source)

Machine-learning enabled acceleration of growth simulations

Daniele Lanzoni, Fabrizio Rovaris, Roberto Bergamaschini, Andrea Fantasia et autres

Simulating the morphological evolution of thin films and nanostructures during deposition has always been a challenge due to the extremely long time scales involved.Experiments, indeed, span typical human scales of seconds or more, while time steps in atomistic molecular dynamics (MD) simulations …

it, fr (code pays fourni par la source)

Extreme time extrapolation capabilities and thermodynamic consistency of physics-inspired neural networks for the 3D microstructure evolution of materials via Cahn–Hilliard flow

Daniele Lanzoni, Andrea Fantasia, Roberto Bergamaschini, Olivier Pierre-Louis et autres

Abstract A Convolutional Recurrent Neural Network (CRNN) is trained to reproduce the evolution of the spinodal decomposition process in three dimensions as described by the Cahn–Hilliard equation. A specialized, physics-inspired architecture is proven to provide close accordance between the predicted evolutions and …

it, fr (code pays fourni par la source)

Surface thermomigration of 2D voids

Stefano Curiotto, Nicolas Combe, Pierre H. Muller, Ali El Barraj et autres

In a thermal gradient, surface nanostructures have been experimentally observed to move due to thermomigration. However, analytical models that describe the thermomigration force acting on surfaces are still controversial. In this work, we start from a thermodynamic approach based on the Massieu …

fr (code pays fourni par la source)

Reinforcement learning with thermal fluctuations at the nanoscale

Francesco Boccardo, Olivier Pierre-Louis

Reinforcement Learning offers a framework to learn to choose actions in order to control a system. However, at small scales Brownian fluctuations limit the control of nanomachine actuation or nanonavigation and of the molecular machinery of life. We analyze this regime using …

fr, it (code pays fourni par la source)

Extreme time extrapolation capabilities and thermodynamic consistency of physics-inspired Neural Networks for the 3D microstructure evolution of materials via Cahn-Hilliard flow

Daniele Lanzoni, Andrea Fantasia, Roberto Bergamaschini, Olivier Pierre-Louis et autres

A Convolutional Recurrent Neural Network (CRNN) is trained to reproduce the evolution of the spinodal decomposition process in three dimensions as described by the Cahn-Hilliard equation. A specialized, physics-inspired architecture is proven to provide close accordance between the predicted evolutions and the …

Reinforcement Learning with thermal fluctuations at the nano-scale

Francesco M. Boccardo, Olivier Pierre-Louis

Reinforcement Learning offers a framework to learn to choose actions in order to achieve some goal. However, at the nano-scale, thermal fluctuations hamper the learning process. We analyze this regime using the general framework of Markov Decision Processes, which applies to a …

Accurate generation of stochastic dynamics based on multi-model generative adversarial networks

Daniele Lanzoni, Olivier Pierre-Louis, Francesco Montalenti

Generative Adversarial Networks (GANs) have shown immense potential in fields such as text and image generation. Only very recently attempts to exploit GANs to statistical-mechanics models have been reported. Here we quantitatively test this approach by applying it to a prototypical stochastic …

it, fr (code pays fourni par la source)

Domain convexification: A simple model for invasion processes

David Martin-Calle, Olivier Pierre-Louis

We propose an invasion model where domains grow up to their convex hulls and merge when they overlap. This model can be seen as a continuum and isotropic counterpart of bootstrap percolation models. From numerical investigations of the model starting with randomly …

fr (code pays fourni par la source)

Depressurization-induced drop breakup through bubble growth

Christophe Pirat, Cécile Cottin-Bizonne, Choongyeop Lee, S.M.M. Ramos et autres

Drop breakup is often associated with boiling or violent impacts onto targets. We report on experiments where the decrease of ambient pressure triggers the growth of a bubble in a drop that sits on a textured hydrophobic surface. We find a transition …

fr, kr (code pays fourni par la source)