SEEDS: Structural Error Estimation in Dynamic System Models

SEEDS：动态系统模型中的结构误差估计

• 批准号: 354645666
• 负责人: Professor Dr. Maik Kschischo
• 金额: --
• 依托单位: Institut für Informatik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2017
• 资助国家: 德国
• 起止时间: 2016-12-31 至 2020-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/354645666?language=en
• 关键词: SEEDS; Structural; Error; Estimation; Dynamic

Understanding and predicting the dynamics of complex systems is a key task in diverse areas, including biology, epidemics, engineering and economics. However, devising sufficiently accurate models for real systems remains a challenging task. Structural model errors caused by insufficient knowledge about the quantitative interactions in the real system and hidden inputs from the environment are fundamental obstacles to model development. In this project, we devise methods and algorithms for reconstructing the root cause of these model errors from data, thereby facilitating the systematic extension and improvement of models. We have recently devised some new criteria for the unique recovery of the model error signal from output measurements. Based on this progress, we aim at designing more robust recovery algorithms with proven guarantees for the accuracy of the reconstruction. As a second main research goal, we will investigate the automatic extension of incomplete models by discovering the governing equations missing in this model. These algorithms will be made available as free software.

理解和预测复杂系统的动态是不同领域的关键任务，包括生物学、流行病、工程学和经济学。然而，为实际系统设计足够精确的模型仍然是一项具有挑战性的任务。由于对实际系统中定量相互作用的认识不足和环境中隐藏的输入所导致的结构性模型误差是模型发展的根本障碍。在本项目中，我们设计了从数据中重建这些模型错误根源的方法和算法，从而促进了模型的系统扩展和改进。我们最近设计了一些新的标准，用于从输出测量中独特地恢复模型误差信号。基于这一进展，我们的目标是设计更健壮的恢复算法，并证明重建的准确性。作为第二个主要研究目标，我们将通过发现模型中缺失的控制方程来研究不完全模型的自动扩展。这些算法将作为免费软件提供。

项目成果

Structural Invertibility and Optimal Sensor Node Placement for Error and Input Reconstruction in Dynamic Systems

• DOI: 10.1103/physrevx.9.041046
• 发表时间: 2019-12-03
• 期刊: PHYSICAL REVIEW X
• 影响因子: 12.5
• 作者: Kahl, Dominik;Wendland, Philipp;Kschischo, Maik
• 通讯作者: Kschischo, Maik

SEEDS: data driven inference of structural model errors and unknown inputs for dynamic systems biology

• DOI: 10.1093/bioinformatics/btaa786
• 发表时间: 2021-05-01
• 期刊: BIOINFORMATICS
• 影响因子: 5.8
• 作者: Newmiwaka, Tobias;Engelhardt, Benjamin;Kschischo, Maik
• 通讯作者: Kschischo, Maik