Differential Equation (DE) is a commonly used modeling method in various scientific subjects such as finance and biology. The parameters in DE models often have interesting scientific interpretations, but their values are often unknown and need to be estimated from the measurements of the DE. In this work, we propose a Bayesian inference framework to solve the problem of estimating the parameters of the DE model, from the given noisy and scarce observations of the solution only. A key issue in this problem is to robustly estimate the derivatives of a function from noisy observations of only the function values at given location points, under the assumption of a physical model in the form of differential equation governing the function and its derivatives. To address the key issue, we use the Gaussian Process Regression with Constraint (GPRC) method which jointly model the solution, the derivatives, and the parametric differential equation, to estimate the solution and its derivatives. For nonlinear differential equations, a Picard-iteration-like approximation of linearization method is used so that the GPRC can be still iteratively applicable. A new potential which combines the data and equation information, is proposed and used in the likelihood for our inference. With numerical examples, we illustrate that the proposed method has competitive performance against existing approaches for estimating the unknown parameters in DEs.
翻译:差别化(DE)是金融和生物学等各种科学学科中常用的一种常用模型方法。 DE模型中的参数往往有有趣的科学解释,但其价值往往不为人所知,需要从DE的测量中估算。 在这项工作中,我们提议了一个巴伊西亚推论框架,以解决从给定的噪音和稀缺的解决方案观测中估算DE模型参数的问题。 这个问题的一个关键问题是,在假定物理模型以不同方程式的形式管理函数及其衍生物时,只对特定地点的函数值进行吵闹观测,对函数值的衍生物进行精确估计,假设这种物理模型的形式是不同的公式,但其价值往往不为人所知,但为了解决关键问题,我们使用高斯进程回归与Constraint(GPRC)的方法,这些方法共同模拟解决方案、衍生物和参数差分数方程式的参数,以估计解决方案及其衍生物。对于非线性方程式来说,使用一种类似于Picard-itelation-sload 方法的函数的衍生物,因此GPRC仍然可以反复适用。为了解决关键问题,我们提出的数字和方程式中的数据和方程式分析方法中所使用的参数是未知的。我们提出的数字方法。