research
∙
05/30/2023
Client: Cross-variable Linear Integrated Enhanced Transformer for Multivariate Long-Term Time Series Forecasting
Long-term time series forecasting (LTSF) is a crucial aspect of modern s...
share
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
∙
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
∙
05/11/2022
AutoKE: An automatic knowledge embedding framework for scientific machine learning
Imposing physical constraints on neural networks as a method of knowledg...
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
∙
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
∙
06/02/2021
KO-PDE: Kernel Optimized Discovery of Partial Differential Equations with Varying Coefficients
Partial differential equations (PDEs) fitting scientific data can repres...
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
∙
02/24/2021
DNN2LR: Automatic Feature Crossing for Credit Scoring
Credit scoring is a major application of machine learning for financial ...
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
∙
08/22/2020
DNN2LR: Interpretation-inspired Feature Crossing for Real-world Tabular Data
For sake of reliability, it is necessary for models in real-world applic...
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
∙
08/03/2019