Data driven discovery of cyber physical systems
Discovery of hybrid dynamical models for real-world cyber-physical systems remains a challenge. This paper proposes a general framework for automating mechanistic modeling of hybrid dynamical systems from observed data with low computational complexity and noise resilience.
| Main Authors: | , , , , , , , |
|---|---|
| פורמט: | Article |
| שפה: | English |
| יצא לאור: |
Nature Portfolio
2019-10-01
|
| סדרה: | Nature Communications |
| גישה מקוונת: | https://doi.org/10.1038/s41467-019-12490-1 |
| סיכום: | Discovery of hybrid dynamical models for real-world cyber-physical systems remains a challenge. This paper proposes a general framework for automating mechanistic modeling of hybrid dynamical systems from observed data with low computational complexity and noise resilience. |
|---|---|
| ISSN: | 2041-1723 |