Consider two halfspaces
The deep operator network (DeepONet) structure has shown great potential in approximating complex solution operators with low generalization errors. Recently, a sequential DeepONet (SDeepONet) was proposed to use sequential learning models in the branch of DeepONet to predict final solutions given timedependent inputs. In the current work, the SDeepONet architecture is extended by modifying the information combination mechanism between the branch and trunk networks to simultaneously predict vector solutions with multiple components at multiple time steps of the evolution history, which is the first in the literature using DeepONets. Two example problems, one on transient fluid flow and the other on pathdependent plastic loading, were shown to demonstrate the capabilities of the model to handle different physics problems. The use of a trained SDeepONet model in inverse parameter identification via the genetic algorithm is shown to demonstrate the application of the model. In almost all cases, the trained model achieved an
 NSFPAR ID:
 10513891
 Publisher / Repository:
 Springer Science + Business Media
 Date Published:
 Journal Name:
 Acta Mechanica
 Volume:
 235
 Issue:
 8
 ISSN:
 00015970
 Format(s):
 Medium: X Size: p. 52575272
 Size(s):
 p. 52575272
 Sponsoring Org:
 National Science Foundation
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