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Found 11 papers, 3 papers with code
Advancing Additive Manufacturing through Deep Learning: A Comprehensive Review of Current Progress and Future Challenges
Additive manufacturing (AM) has already proved itself to be the potential alternative to widely-used subtractive manufacturing due to its extraordinary capacity of manufacturing highly customized products with minimum material wastage.
WOOD: Wasserstein-based Out-of-Distribution Detection
When part of the test samples are drawn from a distribution that is sufficiently far away from that of the training samples (a. k. a.
Failure-averse Active Learning for Physics-constrained Systems
Active learning is a subfield of machine learning that is devised for design and modeling of systems with highly expensive sampling costs.
A Robust Asymmetric Kernel Function for Bayesian Optimization, with Application to Image Defect Detection in Manufacturing Systems
To tackle this challenge, Bayesian optimization, which conducts sequential design via a posterior distribution over the objective function, is a critical method used to find the global optimum of black-box functions.
Partitioned Active Learning for Heterogeneous Systems
Active learning is a subfield of machine learning that focuses on improving the data collection efficiency of expensive-to-evaluate systems.
NP-ODE: Neural Process Aided Ordinary Differential Equations for Uncertainty Quantification of Finite Element Analysis
Finite element analysis (FEA) has been widely used to generate simulations of complex and nonlinear systems.
Neural Network Gaussian Process Considering Input Uncertainty for Composite Structures Assembly
Developing machine learning enabled smart manufacturing is promising for composite structures assembly process.
StressNet: Deep Learning to Predict Stress With Fracture Propagation in Brittle Materials
Catastrophic failure in brittle materials is often due to the rapid growth and coalescence of cracks aided by high internal stresses.
Online Structural Change-point Detection of High-dimensional Streaming Data via Dynamic Sparse Subspace Learning
A tuning method based on Bayesian information criterion and change-point detection accuracy is proposed for penalty coefficients selection.
Tensor decomposition to Compress Convolutional Layers in Deep Learning
Feature extraction for tensor data serves as an important step in many tasks such as anomaly detection, process monitoring, image classification, and quality control.
Active Learning for Gaussian Process Considering Uncertainties with Application to Shape Control of Composite Fuselage
In the machine learning domain, active learning is an iterative data selection algorithm for maximizing information acquisition and improving model performance with limited training samples.
