Development of a regional feature selection-based machine learning system (RFSML v1.0) for air pollution forecasting over China

With the explosive growth of atmospheric data, machine learning models have achieved great success in air pollution forecasting because of their higher computational efficiency than the traditional chemical transport models. However, in previous studies, new prediction algorithms have only been tested at stations or in a small region; a large...

Surrogate-assisted inversion for large-scale history matching: Comparative study between projection-based reduced-order modeling and deep neural network

History matching can play a key role in improving geological characterization and reducing the uncertainty of reservoir model predictions. Application of reservoir history matching is restricted by the huge computational cost by amongst others the many runs of the full model. Surrogate models with a reduced complexity are therefore used to...

Conditioning of Deep-Learning Surrogate Models to Image Data with Application to Reservoir Characterization

Efficient estimation of space varying parameters in numerical models using non-intrusive subdomain reduced order modeling

A reduced order modeling algorithm for the estimation of space varying parameter patterns in numerical models is proposed. In this approach domain decomposition is applied to construct separate approximations to the numerical model in every subdomain. We introduce a new local parameterization that decouples the computational cost of the...

Deep-learning inversion to efficiently handle big-data assimilation: Application to seismic history matching

Seismic history matching can play a key role in geological characterization and uncertainty quantification. However, challenges related to intensive computational demands and complexity restricts its application in many practical cases. This paper presents a conceptual deep-learning-based framework fully deployed in the popular Pytorch...

Non-intrusive subdomain POD-TPWL for reservoir history matching

Systems biology guided by Chinese medicine reveals new markers for sub-typing rheumatoid arthritis patients

BACKGROUND: Complex chronic diseases such as rheumatoid arthritis have become a major challenge in medicine and for the pharmaceutical industry. New impulses for drug development are needed. OBJECTIVE: A systems biology approach is explored to find subtypes of rheumatoid arthritis patients enabling a development towards more personalized...