Novel Physics-Informed Artificial Neural Network Architectures for System and Input Identification of Structural Dynamics PDEs
Herein, two novel Physics Informed Neural Network (PINN) architectures are proposed for output-only system identification and input estimation of dynamic systems. Using merely sparse output-only measurements, the proposed PINNs architectures furnish a novel approach to input, state, and parameter es...
Saved in:
Main Authors: | Sarvin Moradi, Burak Duran, Saeed Eftekhar Azam, Massood Mofid |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2023-02-01
|
Series: | Buildings |
Subjects: | |
Online Access: | https://www.mdpi.com/2075-5309/13/3/650 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Correction: Moradi et al. Novel Physics-Informed Artificial Neural Network Architectures for System and Input Identification of Structural Dynamics PDEs. <i>Buildings</i> 2023, <i>13</i>, 650
by: Sarvin Moradi, et al.
Published: (2025-07-01) -
FEATURES OF USING THE MODIFIED METHOD OF PHYSICAL INPUTS IN ESTIMATING THE SCALES OF SHADOW ACTIVITY IN THE RUSSIAN ECONOMY
by: A. Abroskin, et al.
Published: (2018-11-01) -
A Robust Control Framework for Direct Adaptive State Estimation with Known Inputs for Linear Time-Invariant Dynamic Systems
by: Kevin Fuentes, et al.
Published: (2025-06-01) -
THE PREDICTION OF ENTERPRISES ACTIVITY INDICATORS TAKING INTO ACCOUNT THE INITIAL DATA UNCERTAINTY
by: Yu. Gagarin, et al.
Published: (2019-03-01) -
Item response theory : parameter estimation techniques /
by: Baker, Frank B.
Published: (1992)