# Adaptive Sampling of Dynamic Systems for Generation of Fast and Accurate Surrogate Models

arXiv: https://arxiv.org/abs/2107.13826 · HTML: https://mlsanity.com/paper/2107.13826/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jul 2021.

## Built on by (most cited first, 2 of 2)

- [Physics-based Digital Twins for Autonomous Thermal Food Processing: Efficient, Non-intrusive Reduced-order Modeling](https://mlsanity.com/paper/2209.03062/) (2022)
- [TwinLab: a framework for data-efficient training of non-intrusive reduced-order models for digital twins](https://mlsanity.com/paper/2407.03924/) (2024)

## Abstract (arXiv)

For economic nonlinear model predictive control and dynamic real-time optimization fast and accurate models are necessary. Consequently, the use of dynamic surrogate models to mimic complex rigorous models is increasingly coming into focus. For dynamic systems, the focus so far had been on identifying a system's behavior surrounding a steady-state operation point. In this contribution, we propose a novel methodology to adaptively sample rigorous dynamic process models to generate a dataset for building dynamic surrogate models. The goal of the developed algorithm is to cover an as large as possible area of the feasible region of the original model. To demonstrate the performance of the presented framework it is applied on a dynamic model of a chlor-alkali electrolysis.