Solving nonlinear and complex optimal control problems via multi-task artificial neural networks
Abstract This article proposes a novel approach using multi-task learning for solving nonlinear and complex optimal control problems. A neural network-based framework is proposed by unifying state, control, and adjoint dynamics into the three separate neural networks. A specific structure is designe...
Saved in:
| Main Authors: | Ali Emami Kerdabadi, Alaeddin Malek |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-10339-w |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Multi-task neural network for solving the problem of recognizing the type of QAM and PSK modulation under parametric a priori uncertainty
by: A. A. Paramonov, et al.
Published: (2023-08-01) -
Neural evidence that humans reuse strategies to solve new tasks
by: Sam Hall-McMaster, et al.
Published: (2025-06-01) -
ROBUST NUMERICAL METHODS FOR SOLVING THE poorly determined taskS OF ELECTRODYNAMICS AND NONLINEAR DYNAMICS
by: A. A. Kuraev, et al.
Published: (2019-06-01) -
Towards solving NLP tasks with optimal transport loss
by: Rishabh Bhardwaj, et al.
Published: (2022-11-01) -
APPLICATION OF NEURAL NETWORKS IN ARTIFICIAL INTELLIGENCE TASKS
by: V. F. Antonov, et al.
Published: (2022-08-01)