Web: http://arxiv.org/abs/2206.07756

June 17, 2022, 1:10 a.m. | Shuheng Liao, Tianju Xue, Jihoon Jeong, Samantha Webster, Kornel Ehmann, Jian Cao

cs.LG updates on arXiv.org arxiv.org

Understanding the thermal behavior of additive manufacturing (AM) processes
is crucial for enhancing the quality control and enabling customized process
design. Most purely physics-based computational models suffer from intensive
computational costs, thus not suitable for online control and iterative design
application. Data-driven models taking advantage of the latest developed
computational tools can serve as a more efficient surrogate, but they are
usually trained over a large amount of simulation data and often fail to
effectively use small but high-quality experimental …

arxiv data hybrid lg manufacturing networks neural neural networks physics processes

More from arxiv.org / cs.LG updates on arXiv.org

Machine Learning Researcher - Saalfeld Lab

@ Howard Hughes Medical Institute - Chevy Chase, MD | Ashburn, Virginia

Project Director, Machine Learning in US Health

@ ideas42.org | Remote, US

Data Science Intern

@ NannyML | Remote

Machine Learning Engineer NLP/Speech

@ Play.ht | Remote

Research Scientist, 3D Reconstruction

@ Yembo | Remote, US

Clinical Assistant or Associate Professor of Management Science and Systems

@ University at Buffalo | Buffalo, NY