Scientific Computing for Systems Biology, Faculty of Computer Science, TU Dresden, 01069, Dresden, Germany.
Center for Advancing Electronics Dresden (cfaed), TU Dresden, 01069, Dresden, Germany.
Sci Rep. 2017 Jul 27;7(1):6660. doi: 10.1038/s41598-017-03556-5.
The design of systems or models that work robustly under uncertainty and environmental fluctuations is a key challenge in both engineering and science. This is formalized in the design-centering problem, which is defined as finding a design that fulfills given specifications and has a high probability of still doing so if the system parameters or the specifications fluctuate randomly. Design centering is often accompanied by the problem of quantifying the robustness of a system. Here we present a novel adaptive statistical method to simultaneously address both problems. Our method, L -Adaptation, is inspired by the evolution of robustness in biological systems and by randomized schemes for convex volume computation. It is able to address both problems in the general, non-convex case and at low computational cost. We describe the concept and the algorithm, test it on known benchmarks, and demonstrate its real-world applicability in electronic and biological systems. In all cases, the present method outperforms the previous state of the art. This enables re-formulating optimization problems in engineering and biology as design centering problems, taking global system robustness into account.
在工程和科学领域,设计能够在不确定性和环境波动下稳健运行的系统或模型是一项关键挑战。这在设计中心问题中得到了正式表述,该问题定义为找到一种设计,使其既能满足给定的规范,又能在系统参数或规范随机波动时仍有很高的满足概率。设计中心问题通常伴随着量化系统鲁棒性的问题。在这里,我们提出了一种新颖的自适应统计方法,可以同时解决这两个问题。我们的方法 L- Adaptation 受到生物系统中鲁棒性进化和凸体积计算随机方案的启发。它能够在一般非凸情况下以低计算成本解决这两个问题。我们描述了该方法的概念和算法,在已知基准上进行了测试,并在电子和生物系统中展示了其实际应用。在所有情况下,本方法都优于先前的技术水平。这使得可以将工程和生物学中的优化问题重新表述为设计中心问题,同时考虑全局系统鲁棒性。