Deep transfer operator learning for partial differential equations under conditional shift
Transfer learning enables the transfer of knowledge gained while learning to perform one
task (source) to a related but different task (target), hence addressing the expense of data …
task (source) to a related but different task (target), hence addressing the expense of data …
Source-Free Multidomain Adaptation With Fuzzy Rule-Based Deep Neural Networks
Unsupervised domain adaptation deals with a task from an unlabeled target domain by
leveraging the knowledge gained from labeled source domain (s). The fuzzy system is …
leveraging the knowledge gained from labeled source domain (s). The fuzzy system is …
An extremely simple algorithm for source domain reconstruction
The aim of unsupervised domain adaptation (UDA) is to utilize knowledge from a source
domain to enhance the performance of a given target domain. Due to the lack of accessibility …
domain to enhance the performance of a given target domain. Due to the lack of accessibility …
[PDF][PDF] Deep transfer learning for partial differential equations under conditional shift with DeepONet
Traditional machine learning algorithms are designed to learn in isolation, ie address single
tasks. The core idea of transfer learning (TL) is that knowledge gained in learning to perform …
tasks. The core idea of transfer learning (TL) is that knowledge gained in learning to perform …
A meta-invariant feature space method for accurate tool wear prediction under cross conditions
C Liu, Y Li, J Li, J Hua - IEEE Transactions on Industrial …, 2021 - ieeexplore.ieee.org
Cross conditions prediction is a prevalent problem in manufacturing area, where tool wear
prediction is a typical one. Existing data-driven methods for tool wear prediction mainly focus …
prediction is a typical one. Existing data-driven methods for tool wear prediction mainly focus …
Multi-condition wear prediction and assessment of milling cutters based on linear discriminant analysis and ensemble methods
H Zhou, S Gao, Y **e, C Zhang, J Liu - Measurement, 2023 - Elsevier
Accurate prediction of tool wear in multi-conditions is still a thorny problem, and rapid and
accurate construction of prediction models for multi-conditions is an essential part of …
accurate construction of prediction models for multi-conditions is an essential part of …
Sampling via the aggregation value for data-driven manufacturing
Data-driven modelling has shown promising potential in many industrial applications, while
the expensive and time-consuming labelling of experimental and simulation data restricts its …
the expensive and time-consuming labelling of experimental and simulation data restricts its …
CME-EPC: A coarse-mechanism embedded error prediction and compensation framework for robot multi-condition tasks
T Zhang, F Peng, X Tang, R Yan, R Deng - Robotics and Computer …, 2024 - Elsevier
While industrial robots are widely used in various fields owing to their large workspace and
high flexibility, significant errors constrain their application in high-precision scenarios …
high flexibility, significant errors constrain their application in high-precision scenarios …
Multiple source partial knowledge transfer for manufacturing system modelling
Transfer learning has shown its attractiveness for manufacturing system modelling by
leveraging previously acquired knowledge to assist in training the target model, whereas …
leveraging previously acquired knowledge to assist in training the target model, whereas …
Predicting demands of COVID-19 prevention and control materials via co-evolutionary transfer learning
Q Song, YJ Zheng, J Yang, YJ Huang… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
The novel coronavirus pneumonia (COVID-19) has created great demands for medical
resources. Determining these demands timely and accurately is critically important for the …
resources. Determining these demands timely and accurately is critically important for the …