An Application of Random Projection to Parameter Estimation in Partial Differential Equations

2012 ◽  
Vol 34 (4) ◽  
pp. A2344-A2365 ◽  
Author(s):  
Joseph Young ◽  
Denis Ridzal
Keyword(s):  
Parameter Estimation ◽  
Partial Differential Equations ◽  
Differential Equations ◽  
Random Projection ◽  
Partial Differential

scholarly journals PDEparams: Parameter fitting toolbox for partial differential equations in Python

2019 ◽  
Author(s):  
César Parra-Rojas ◽  
Esteban A. Hernandez-Vargas
Keyword(s):  
Parameter Estimation ◽  
Partial Differential Equations ◽  
Differential Equations ◽  
Biological Systems ◽  
Source Code ◽  
Parameter Analysis ◽  
Partial Differential ◽  
Parameter Fitting ◽  
Link Type ◽  
Boot Strapping

AbstractMotivationPartial differential equations (PDEs) is a well-established and powerful tool to simulate multi-cellular biological systems. However, available free tools for validation against data are not established. ThePDEparamsmodule provides flexible functionality in Python for parameter estimation in PDE models.ResultsThePDEparamsmodule provides a flexible interface and readily accommodates different parameter analysis tools in PDE models such as computation of likelihood profiles, and parametric boot-strapping, along with direct visualisation of the results. To our knowledge, it is the first open, freely available tool for parameter fitting of PDE models.Availability and implementationThePDEparamsmodule is distributed under the MIT license. The source code, usage instructions and step-by-step examples are freely available on GitHub atgithub.com/systemsmedicine/[email protected]

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