Long-Time Predictive Modeling of Nonlinear Dynamical Systems Using Neural Networks
We study the use of feedforward neural networks (FNN) to develop models of nonlinear dynamical systems from data. Emphasis is placed on predictions at long times, with limited data availability. Inspired by global stability analysis, and the observation of strong correlation between the local error...
Saved in:
| Main Authors: | Shaowu Pan, Karthik Duraisamy |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/4801012 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Use of Dynamic Neural Networks for Modeling Nonlinear Objects with Significant Nonlinearity
by: Oleksandr Fomin, et al.
Published: (2023-10-01) -
Dynamic Neural Network Identification and Decoupling Control Approach for MIMO Time-Varying Nonlinear Systems
by: Zhixi Shen, et al.
Published: (2014-01-01) -
Discretization-independent surrogate modeling of physical fields around variable geometries using coordinate-based networks
by: James Duvall, et al.
Published: (2025-01-01) -
Adaptive Control of Nonlinear Discrete-Time Systems by Using OS-ELM Neural Networks
by: Xiao-Li Li, et al.
Published: (2014-01-01) -
Physics-informed neural networks for a highly nonlinear dynamic system
by: Ruxandra Barbulescu, et al.
Published: (2025-04-01)