Search Results for author:
Found 10 papers, 6 papers with code
A novel Fourier neural operator framework for classification of multi-sized images: Application to three dimensional digital porous media
First, we show that this approach is only effective for porous media with fixed sizes, whereas it fails for porous media of varying sizes.
Prediction of Effective Elastic Moduli of Rocks using Graph Neural Networks
This study presents a Graph Neural Networks (GNNs)-based approach for predicting the effective elastic moduli of rocks from their digital CT-scan images.
Physics-informed PointNet: On how many irregular geometries can it solve an inverse problem simultaneously? Application to linear elasticity
In this article, we demonstrate that PIPN predicts the solution of desired partial differential equations over a few hundred domains simultaneously, while it only uses sparse labeled data.
ChatGPT for Programming Numerical Methods
Specifically, we examine the capability of GhatGPT for generating codes for numerical algorithms in different programming languages, for debugging and improving written codes by users, for completing missed parts of numerical codes, rewriting available codes in other programming languages, and for parallelizing serial codes.
Direct Estimation of Porosity from Seismic Data using Rock and Wave Physics Informed Neural Networks (RW-PINN)
We present a rock and wave physics informed neural network (RW-PINN) model that can estimate porosity directly from seismic image traces with no or limited number of wells, with predictions that are consistent with rock physics and geologic knowledge of deposition.
Point-Cloud Deep Learning of Porous Media for Permeability Prediction
We compare our deep learning strategy with a convolutional neural network from various perspectives, specifically for maximum possible batch size.
Improved POMDP Tree Search Planning with Prioritized Action Branching
Online solvers for partially observable Markov decision processes have difficulty scaling to problems with large action spaces.
Bayesian Optimized Monte Carlo Planning
Monte Carlo tree search with progressive widening attempts to improve scaling by sampling from the action space to construct a policy search tree.
Real-Time Well Log Prediction From Drilling Data Using Deep Learning
The objective is to study the feasibility of predicting subsurface rock properties in wells from real-time drilling data.
Seismic Bayesian evidential learning: Estimation and uncertainty quantification of sub-resolution reservoir properties
We present a framework that enables estimation of low-dimensional sub-resolution reservoir properties directly from seismic data, without requiring the solution of a high dimensional seismic inverse problem.
