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Simultant:使用解析梯度计算同时对函数和微分方程进行曲线拟合。

Simultant: simultaneous curve fitting of functions and differential equations using analytical gradient calculations.

机构信息

Niels Bohr Institute, University of Copenhagen, 2100, Copenhagen, Denmark.

出版信息

BMC Bioinformatics. 2022 May 21;23(1):191. doi: 10.1186/s12859-022-04728-5.

Abstract

BACKGROUND

The initial step in comparing mathematical models to experimental data is to do a fit. This process can be complicated when either the mathematical models are not analytically solvable (e.g. because of nonlinear differential equations) or when the relation between data and models is complex (e.g. when some fitting parameters must be shared between many data sets).

RESULTS

We introduce Simultant, a software package that allows complex fitting setups to be easily defined using a simple graphical user interface. Fitting functions can be defined directly as mathematical expressions or indirectly as the solution to specified ordinary differential equations. Analytical gradients of these functions, including the solution of differential equations, are automatically calculated to provide fast fitting even for functions with many parameters. The software enables easy definition of complex fitting setups in which parameters can be shared across both data sets and models to allow simultaneous fits to be performed.

CONCLUSIONS

Simultant exploits differentiable programming and simplifies modern fitting approaches in a unified graphical interface.

摘要

背景

将数学模型与实验数据进行比较的初始步骤是进行拟合。当数学模型无法进行解析求解(例如,由于存在非线性微分方程)或数据与模型之间的关系复杂(例如,当许多数据集之间必须共享某些拟合参数时)时,拟合过程可能会变得复杂。

结果

我们引入了 Simultant,这是一个软件包,它允许使用简单的图形用户界面轻松定义复杂的拟合设置。拟合函数可以直接定义为数学表达式,也可以间接定义为指定常微分方程的解。这些函数的解析梯度,包括微分方程的求解,会自动计算,以便即使对于具有许多参数的函数,也能实现快速拟合。该软件可以轻松定义复杂的拟合设置,其中可以在数据集和模型之间共享参数,以允许同时进行拟合。

结论

Simultant 利用可微分编程,并在统一的图形界面中简化了现代拟合方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9807/9124400/b04e07e4d406/12859_2022_4728_Fig1_HTML.jpg

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