# Data-driven Discovery of Cyber-Physical Systems

arXiv: https://arxiv.org/abs/1810.00697 · HTML: https://mlsanity.com/paper/1810.00697/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2018.

## Built on by (most cited first, 13 of 13)

- [Sparse Bayesian Deep Learning for Dynamic System Identification](https://mlsanity.com/paper/2107.12910/) (2021)
- [Bounded nonlinear forecasts of partially observed geophysical systems with physics-constrained deep learning](https://mlsanity.com/paper/2202.05750/) (2022)
- [DeceFL: A Principled Decentralized Federated Learning Framework](https://mlsanity.com/paper/2107.07171/) (2021)
- [Dynamic financial processes identification using sparse regressive reservoir computers](https://mlsanity.com/paper/2310.12144/) (2023)
- [Identifiability of Differential-Algebraic Systems](https://mlsanity.com/paper/2405.13818/) (2024)
- [SODAs: Sparse Optimization for the Discovery of Differential and Algebraic Equations](https://mlsanity.com/paper/2503.05993/) (2025)
- [Logic-informed reinforcement learning for cross-domain optimization of large-scale cyber-physical systems](https://mlsanity.com/paper/2511.00806/) (2025)
- [Interpretable System Identification and Long-term Prediction on Time-Series Data](https://mlsanity.com/paper/2303.01193/) (2023)
- [Automatically identifying ordinary differential equations from data](https://mlsanity.com/paper/2304.11182/) (2023)
- [Identifying Systems with Symmetries using Equivariant Autoregressive Reservoir Computers](https://mlsanity.com/paper/2311.09511/) (2023)
- [SRLR: Symbolic Regression based Logic Recovery to Counter Programmable Logic Controller Attacks](https://mlsanity.com/paper/2512.11298/) (2025)
- [GIMLET: Generalizable and Interpretable Model Learning through Embedded Thermodynamics](https://mlsanity.com/paper/2512.19936/) (2025)
- [One-shot learning for the complex dynamical behaviors of weakly nonlinear forced oscillators](https://mlsanity.com/paper/2604.15181/) (2026)

## Abstract (arXiv)

Cyber-physical systems (CPSs) embed software into the physical world. They appear in a wide range of applications such as smart grids, robotics, intelligent manufacture and medical monitoring. CPSs have proved resistant to modeling due to their intrinsic complexity arising from the combination of physical components and cyber components and the interaction between them. This study proposes a general framework for reverse engineering CPSs directly from data. The method involves the identification of physical systems as well as the inference of transition logic. It has been applied successfully to a number of real-world examples ranging from mechanical and electrical systems to medical applications. The novel framework seeks to enable researchers to make predictions concerning the trajectory of CPSs based on the discovered model. Such information has been proven essential for the assessment of the performance of CPS, the design of failure-proof CPS and the creation of design guidelines for new CPSs.