Unveiling the underlying governing equations of nonlinear dynamic system...
A new research framework is proposed to incorporate machine learning
tec...
Electrical energy is essential in today's society. Accurate electrical l...
The working mechanisms of complex natural systems tend to abide by conci...
Data-driven discovery of PDEs has made tremendous progress recently, and...
An algorithm named InterOpt for optimizing operational parameters is pro...
Uncertainty quantification (UQ) of subsurface two-phase flow usually req...
Imposing physical constraints on neural networks as a method of knowledg...
The Li-ion battery is a complex physicochemical system that generally ta...
Identification of unknown physical processes and parameters of groundwat...
The interpretability of deep neural networks has attracted increasing
at...
To maximize the economic benefits of geothermal energy production, it is...
Large-scale or high-resolution geologic models usually comprise a huge n...
We build surrogate models for dynamic 3D subsurface single-phase flow
pr...
Accurate forecasts of photovoltaic power generation (PVPG) are essential...
Particle settling in inclined channels is an important phenomenon that o...
Random reconstruction of three-dimensional (3D) digital rocks from
two-d...
Partial differential equations (PDEs) are concise and understandable
rep...
Although deep-learning has been successfully applied in a variety of sci...
Data-driven discovery of partial differential equations (PDEs) has achie...
Machine learning models have been successfully used in many scientific a...
Data-driven discovery of partial differential equations (PDEs) has attra...
A Theory-guided Auto-Encoder (TgAE) framework is proposed for surrogate
...
Deep neural networks (DNNs) are widely used as surrogate models in
geoph...
The theory-guided neural network (TgNN) is a kind of method which improv...
Deep-learning has achieved good performance and shown great potential fo...
Data-driven methods have recently made great progress in the discovery o...
This study proposes a supervised learning method that does not rely on
l...
In this study, we propose an ensemble long short-term memory (EnLSTM)
ne...
Subsurface flow problems usually involve some degree of uncertainty.
Con...
Data-driven methods have recently been developed to discover underlying
...
In recent years, data-driven methods have been utilized to learn dynamic...
In this study, an efficient stochastic gradient-free method, the ensembl...
With the advent of modern data collection and storage technologies,
data...