论文标题
机械建模中数据一致反转的新颖而灵活的参数估计方法
Novel and flexible parameter estimation methods for data-consistent inversion in mechanistic modeling
论文作者
论文摘要
对物理系统的预测通常依赖于从实体团体中获取的知识，例如生物科学中细胞的集合。为了定性和定量分析，这些集合是通过机械模型（MM）的参数族模拟的。基于贝叶斯推断和模型人群的两类方法目前在物理系统的参数估计中占上风。但是，在贝叶斯分析中，MM参数的非信息先验会引入不良偏见。在这里，我们提出了如何在随机反问题（SIP）的框架内推断参数，也称为数据一致性反转，其中，先前的目标仅由于MM不可逆性而引起的不确定性。为了证明，我们引入了基于拒绝采样，马尔可夫链蒙特卡洛和生成对抗网络（GAN）（GAN）的新方法来解决SIP。此外，为了克服SIP的局限性，我们根据受限的优化重新重新重新制定SIP，并提出一种新颖的GAN来解决约束优化问题。
Predictions for physical systems often rely upon knowledge acquired from ensembles of entities, e.g., ensembles of cells in biological sciences. For qualitative and quantitative analysis, these ensembles are simulated with parametric families of mechanistic models (MM). Two classes of methodologies, based on Bayesian inference and Population of Models, currently prevail in parameter estimation for physical systems. However, in Bayesian analysis, uninformative priors for MM parameters introduce undesirable bias. Here, we propose how to infer parameters within the framework of stochastic inverse problems (SIP), also termed data-consistent inversion, wherein the prior targets only uncertainties that arise due to MM non-invertibility. To demonstrate, we introduce new methods to solve SIP based on rejection sampling, Markov chain Monte Carlo, and generative adversarial networks (GANs). In addition, to overcome limitations of SIP, we reformulate SIP based on constrained optimization and present a novel GAN to solve the constrained optimization problem.
