Optimal Experimental Design for Uncertain Systems Based on Coupled Differential Equations
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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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Jul 2020.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 1.
4 papers.
- 2022Multi-Objective Latent Space Optimization of Generative Molecular Design Modelscited by 13
- 2021Robust Importance Sampling for Error Estimation in the Context of Optimal Bayesian Transfer Learningcited by 1
- 2023A Rigorous Uncertainty-Aware Quantification Framework Is Essential for Reproducible and Replicable Machine Learning Workflowscited by 1
- 2022Neural Message Passing for Objective-Based Uncertainty Quantification and Optimal Experimental Designcited by 0
- 2023A Rigorous Uncertainty-Aware Quantification Framework Is Essential for Reproducible and Replicable Machine Learning Workflows13 Jan 2023
- 2022Neural Message Passing for Objective-Based Uncertainty Quantification and Optimal Experimental Design14 Mar 2022
- 2022Multi-Objective Latent Space Optimization of Generative Molecular Design Models1 Mar 2022
- 2021Robust Importance Sampling for Error Estimation in the Context of Optimal Bayesian Transfer Learning5 Sep 2021