Search Results for author:
Found 5 papers, 0 papers with code
A Novel A.I Enhanced Reservoir Characterization with a Combined Mixture of Experts -- NVIDIA Modulus based Physics Informed Neural Operator Forward Model
Additionally, the PINO-Res-Sim in the aREKI workflow efficiently recovered unknown fields with a computational speedup of 100 to 6000 times faster than conventional methods.
A Novel Cluster Classify Regress Model Predictive Controller Formulation; CCR-MPC
The methodology involves developing a time-series machine learning model with either a Long Short Term Memory model (LSTM) or a Gradient Boosting Algorithm (XGboost), capable of forecasting this weather states for any desired time horizon and concurrently optimising the control signals to the desired set point.
Fast Deep Mixtures of Gaussian Process Experts
Mixtures of experts have become an indispensable tool for flexible modelling in a supervised learning context, allowing not only the mean function but the entire density of the output to change with the inputs.
4D Seismic History Matching Incorporating Unsupervised Learning
This novel combination of techniques from machine learning, sparsity regularisation, seismic imaging and history matching aims to address the ill-posedness of the inversion of historical production data efficiently using ES-MDA.
Cluster, Classify, Regress: A General Method For Learning Discountinous Functions
This paper presents a method for solving the supervised learning problem in which the output is highly nonlinear and discontinuous.
