Convoluted filtering for process cycle modeling
Abstract Principles of materials science and engineering, physics, mathematics, and information science are used to extract knowledge and insights from the process‐structure–property‐performance relationships hidden in materials data. The process‐structure modeling can be accelerated without loss of...
Main Author: | Vyacheslav Romanov |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2023-11-01
|
Series: | Engineering Reports |
Subjects: | |
Online Access: | https://doi.org/10.1002/eng2.12657 |
Similar Items
-
An Unsupervised Fault Warning Method Based on Hybrid Information Gain and a Convolutional Autoencoder for Steam Turbines
by: Jinxing Zhai, et al.
Published: (2024-08-01) -
Causally nonseparable processes admitting a causal model
by: Adrien Feix, et al.
Published: (2016-01-01) -
Learning in Convolutional Neural Networks Accelerated by Transfer Entropy
by: Adrian Moldovan, et al.
Published: (2021-09-01) -
STHSGCN: Spatial-temporal heterogeneous and synchronous graph convolution network for traffic flow prediction
by: Xian Yu, et al.
Published: (2023-09-01) -
Causality Analysis with Information Geometry: A Comparison
by: Heng Jie Choong, et al.
Published: (2023-05-01)