research
∙
09/14/2023
Physics-constrained robust learning of open-form PDEs from limited and noisy data
Unveiling the underlying governing equations of nonlinear dynamic system...
share
research
∙
11/07/2022
Retention Time Prediction for Chromatographic Enantioseparation by Quantile Geometry-enhanced Graph Neural Network
A new research framework is proposed to incorporate machine learning tec...
research
∙
10/05/2022
TgDLF2.0: Theory-guided deep-learning for electrical load forecasting via Transformer and transfer learning
Electrical energy is essential in today's society. Accurate electrical l...
research
∙
10/04/2022
DISCOVER: Deep identification of symbolic open-form PDEs via enhanced reinforcement-learning
The working mechanisms of complex natural systems tend to abide by conci...
research
∙
08/05/2022
Discovery of partial differential equations from highly noisy and sparse data with physics-informed information criterion
Data-driven discovery of PDEs has made tremendous progress recently, and...
research
∙
06/20/2022
Interpretable machine learning optimization (InterOpt) for operational parameters: a case study of highly-efficient shale gas development
An algorithm named InterOpt for optimizing operational parameters is pro...
research
∙
05/28/2022
Uncertainty quantification of two-phase flow in porous media via coupled-TgNN surrogate model
Uncertainty quantification (UQ) of subsurface two-phase flow usually req...
research
∙
05/11/2022
AutoKE: An automatic knowledge embedding framework for scientific machine learning
Imposing physical constraints on neural networks as a method of knowledg...
research
∙
05/06/2022
Inferring electrochemical performance and parameters of Li-ion batteries based on deep operator networks
The Li-ion battery is a complex physicochemical system that generally ta...
research
∙
04/30/2022
Identification of Physical Processes and Unknown Parameters of 3D Groundwater Contaminant Problems via Theory-guided U-net
Identification of unknown physical processes and parameters of groundwat...
research
∙
04/16/2022
Semantic interpretation for convolutional neural networks: What makes a cat a cat?
The interpretability of deep neural networks has attracted increasing at...
research
∙
04/15/2022
Deep learning based closed-loop optimization of geothermal reservoir production
To maximize the economic benefits of geothermal energy production, it is...
research
∙
12/31/2021
Deep-learning-based upscaling method for geologic models via theory-guided convolutional neural network
Large-scale or high-resolution geologic models usually comprise a huge n...
research
∙
11/14/2021
Uncertainty quantification and inverse modeling for subsurface flow in 3D heterogeneous formations using a theory-guided convolutional encoder-decoder network
We build surrogate models for dynamic 3D subsurface single-phase flow pr...
research
∙
09/28/2021
An Adaptive Deep Learning Framework for Day-ahead Forecasting of Photovoltaic Power Generation
Accurate forecasts of photovoltaic power generation (PVPG) are essential...
research
∙
09/25/2021
Constructing Sub-scale Surrogate Model for Proppant Settling in Inclined Fractures from Simulation Data with Multi-fidelity Neural Network
Particle settling in inclined channels is an important phenomenon that o...
research
∙
08/05/2021
RockGPT: Reconstructing three-dimensional digital rocks from single two-dimensional slice from the perspective of video generation
Random reconstruction of three-dimensional (3D) digital rocks from two-d...
research
∙
06/09/2021
Any equation is a forest: Symbolic genetic algorithm for discovering open-form partial differential equations (SGA-PDE)
Partial differential equations (PDEs) are concise and understandable rep...
research
∙
05/31/2021
Deep-Learning Discovers Macroscopic Governing Equations for Viscous Gravity Currents from Microscopic Simulation Data
Although deep-learning has been successfully applied in a variety of sci...
research
∙
05/31/2021
Robust discovery of partial differential equations in complex situations
Data-driven discovery of partial differential equations (PDEs) has achie...
research
∙
12/11/2020
Theory-guided hard constraint projection (HCP): a knowledge-based data-driven scientific machine learning method
Machine learning models have been successfully used in many scientific a...
research
∙
11/24/2020
Deep-learning based discovery of partial differential equations in integral form from sparse and noisy data
Data-driven discovery of partial differential equations (PDEs) has attra...
research
∙
11/17/2020
Theory-guided Auto-Encoder for Surrogate Construction and Inverse Modeling
A Theory-guided Auto-Encoder (TgAE) framework is proposed for surrogate ...
research
∙
09/08/2020
Weak Form Theory-guided Neural Network (TgNN-wf) for Deep Learning of Subsurface Single and Two-phase Flow
Deep neural networks (DNNs) are widely used as surrogate models in geoph...
research
∙
08/24/2020
A Lagrangian Dual-based Theory-guided Deep Neural Network
The theory-guided neural network (TgNN) is a kind of method which improv...
research
∙
07/28/2020
Deep-Learning based Inverse Modeling Approaches: A Subsurface Flow Example
Deep-learning has achieved good performance and shown great potential fo...
research
∙
05/16/2020
Deep-learning of Parametric Partial Differential Equations from Sparse and Noisy Data
Data-driven methods have recently made great progress in the discovery o...
research
∙
04/26/2020
Physics-constrained indirect supervised learning
This study proposes a supervised learning method that does not rely on l...
research
∙
04/26/2020
Ensemble long short-term memory (EnLSTM) network
In this study, we propose an ensemble long short-term memory (EnLSTM) ne...
research
∙
04/25/2020
Efficient Uncertainty Quantification for Dynamic Subsurface Flow with Surrogate by Theory-guided Neural Network
Subsurface flow problems usually involve some degree of uncertainty. Con...
research
∙
01/21/2020
DLGA-PDE: Discovery of PDEs with incomplete candidate library via combination of deep learning and genetic algorithm
Data-driven methods have recently been developed to discover underlying ...
research
∙
08/13/2019
DL-PDE: Deep-learning based data-driven discovery of partial differential equations from discrete and noisy data
In recent years, data-driven methods have been utilized to learn dynamic...
research
∙
08/03/2019
Ensemble Neural Networks (ENN): A gradient-free stochastic method
In this study, an efficient stochastic gradient-free method, the ensembl...
research
∙
10/29/2018