# Optimal Experimental Design for Uncertain Systems Based on Coupled Differential Equations

arXiv: https://arxiv.org/abs/2007.06117 · HTML: https://mlsanity.com/paper/2007.06117/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jul 2020.

## Built on by (most cited first, 4 of 4)

- [Multi-Objective Latent Space Optimization of Generative Molecular Design Models](https://mlsanity.com/paper/2203.00526/) (2022)
- [Robust Importance Sampling for Error Estimation in the Context of Optimal Bayesian Transfer Learning](https://mlsanity.com/paper/2109.02150/) (2021)
- [A Rigorous Uncertainty-Aware Quantification Framework Is Essential for Reproducible and Replicable Machine Learning Workflows](https://mlsanity.com/paper/2301.05763/) (2023)
- [Neural Message Passing for Objective-Based Uncertainty Quantification and Optimal Experimental Design](https://mlsanity.com/paper/2203.07120/) (2022)

## Abstract (arXiv)

We consider the optimal experimental design problem for an uncertain Kuramoto model, which consists of N interacting oscillators described by coupled ordinary differential equations. The objective is to design experiments that can effectively reduce the uncertainty present in the coupling strengths between the oscillators, thereby minimizing the cost of robust control of the uncertain Kuramoto model. We demonstrate the importance of quantifying the operational impact of the potential experiments in designing optimal experiments.