There is a large literature of numerical methods for phase field models from materials science. The prototype models are the Allen-Cahn and Cahn-Hilliard equa- tions. We present four benchmark problems for these equations, with numerical results validated using several computational methods with different spatial and temporal discretizations. Our goal is to provide the scientific community with a reliable reference point for assessing the accuracy and reliability of future software for this important class of problem.
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A novel sequential method to train physics informed neural networks for Allen Cahn and Cahn Hilliard equations
- Award ID(s):
- 1937983
- PAR ID:
- 10322723
- Date Published:
- Journal Name:
- Computer Methods in Applied Mechanics and Engineering
- Volume:
- 390
- Issue:
- C
- ISSN:
- 0045-7825
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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