FATODE
FATODE: a library for forward, adjoint, and tangent linear integration of ODEs. Fatode is a Fortran library for the integration of ordinary differential equations with direct and adjoint sensitivity analysis capabilities. The paper describes the capabilities, implementation, code organization, and usage of this package. Fatode implements four families of methods: explicit Runge-Kutta for nonstiff problems, fully implicit Runge-Kutta, singly diagonally implicit Runge-Kutta, and Rosenbrock for stiff problems. Each family contains several methods with different orders of accuracy; users can add new methods by simply providing their coefficients. For each family the forward, adjoint, and tangent linear models are implemented. General purpose solvers for dense and sparse linear algebra are used; users can easily incorporate problem-tailored linear algebra routines. The performance of the package is demonstrated on several test problems. To the best of our knowledge Fatode is the first publicly available general purpose package that offers forward and adjoint sensitivity analysis capabilities in the context of Runge-Kutta methods. A wide range of applications is expected to benefit from its use; examples include parameter estimation, data assimilation, optimal control, and uncertainty quantification.
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References in zbMATH (referenced in 8 articles )
Showing results 1 to 8 of 8.
Sorted by year (- Zhang, Hong; Constantinescu, Emil M.; Smith, Barry F.: \textttPETScTSAdjoint: a discrete adjoint ODE solver for first-order and second-order sensitivity analysis (2022)
- Chris Rackauckas, Mike Innes, Yingbo Ma, Jesse Bettencourt, Lyndon White, Vaibhav Dixit: DiffEqFlux.jl - A Julia Library for Neural Differential Equations (2019) arXiv
- Farrell, P. E.; Hake, J. E.; Funke, S. W.; Rognes, M. E.: Automated adjoints of coupled PDE-ODE systems (2019)
- Sanguinetti, Guido (ed.); Huynh-Thu, Vân Anh (ed.): Gene regulatory networks. Methods and protocols (2019)
- Römer, Ulrich; Narayanamurthi, Mahesh; Sandu, Adrian: Solving parameter estimation problems with discrete adjoint exponential integrators (2018)
- Ahmed Attia, Adrian Sandu: DATeS: A Highly-Extensible Data Assimilation Testing Suite (2017) arXiv
- Rao, Vishwas; Sandu, Adrian: A time-parallel approach to strong-constraint four-dimensional variational data assimilation (2016)
- Zhang, Hong; Sandu, Adrian: FATODE: a library for forward, adjoint, and tangent linear integration of ODEs (2014)