56 citations · 64 across the 4 of their papers we have counts for
4 papers
Physics-Informed Neural Networks for Methane Sorption: Cross-Gas Transfer Learning, Ensemble Collapse Under Physics Constraints, and Monte Carlo Dropout Uncertainty Quantification
Mohammad Nooraiepour, Zezhang Song, Wei Li +1
Accurate methane sorption prediction across heterogeneous coal ranks requires models that combine thermodynamic consistency, efficient knowledge transfer across data-scarce geologi…
A semi-Lagrangian method for the direct numerical simulation of crystallization and precipitation at the pore scale
Sarah Perez, Jean-Matthieu Etancelin, Philippe Poncet
This article introduces a new efficient particle method for the numerical simulation of crystallization and precipitation at the pore scale of real rock geometries extracted by X-R…
Auto-weighted Bayesian Physics-Informed Neural Networks and robust estimations for multitask inverse problems in pore-scale imaging of dissolution
Sarah Perez, Philippe Poncet
In this article, we present a novel data assimilation strategy in pore-scale imaging and demonstrate that this makes it possible to robustly address reactive inverse problems incor…
Adaptive weighting of Bayesian physics informed neural networks for multitask and multiscale forward and inverse problems
Sarah Perez, Suryanarayana Maddu, Ivo F. Sbalzarini +1
In this paper, we present a novel methodology for automatic adaptive weighting of Bayesian Physics-Informed Neural Networks (BPINNs), and we demonstrate that this makes it possible…