Forouzesh Negin, Watson Layne T, Onufriev Alexey V
Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061.
Department of Computer Science, Department of Mathematics, Department of Aerospace and Ocean Engineering, Center for Soft Matter and Biological Physics, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061.
Spring Simul Conf. 2020 May;2020. doi: 10.22360/springsim.2020.hpc.004. Epub 2020 May 19.
In multidimensional parameter optimization of complex systems, the preferred solution must also be robust to virtually inevitable perturbations and uncertainties. Having a conceptually simple and computationally facile metric that can help distinguish between candidate optimum solutions in a post processing step is useful. Motivated by free energy function in statistical physics, which evaluates a trade-off between entropy and enthalpy, here we introduce a novel statistical robustness metric that assesses robustness with respect to possible to inevitable uncertainties in the objective function values or optimal parameters. The metric is the expected value of the objective function, evaluated using weighted samples in a box around each optimum. The width of the sample distribution is problem-specific. As a case study, the proposed robustness metric is employed to find the most robust optimal solution in 5-dimensional parameter space in the context of dielectric boundary optimization in atomistic modeling, relevant to computational drug discovery.
在复杂系统的多维参数优化中，首选解决方案还必须对几乎不可避免的扰动和不确定性具有鲁棒性。拥有一个概念简单且计算简便的度量标准，有助于在后期处理步骤中区分候选最优解，这是很有用的。受统计物理学中自由能函数的启发，该函数评估熵与焓之间的权衡，在此我们引入一种新颖的统计鲁棒性度量标准，该标准针对目标函数值或最优参数中可能到不可避免的不确定性来评估鲁棒性。该度量标准是目标函数的期望值，使用围绕每个最优值的盒子中的加权样本进行评估。样本分布的宽度是特定于问题的。作为一个案例研究，在与计算药物发现相关的原子模型中的介电边界优化背景下，所提出的鲁棒性度量标准被用于在5维参数空间中找到最鲁棒的最优解。
