Uniformly high order accurate essentially non-oscillatory schemes, 111
Journal of Computational Physics
ENO schemes with subcell resolution
Journal of Computational Physics
High resolution schemes for steady flow computation
Journal of Computational Physics
Algorithm 746: PCOMP—a Fortran code for automatic differentiation
ACM Transactions on Mathematical Software (TOMS)
Algorithm 573: NL2SOL—An Adaptive Nonlinear Least-Squares Algorithm [E4]
ACM Transactions on Mathematical Software (TOMS)
Remark on algorithm 746: new features of PCOMP, a Fortran code for automatic differentiation
ACM Transactions on Mathematical Software (TOMS)
Numerical Data Fitting in Dynamical Systems: A Practical Introduction with Applications and Software
Numerical Data Fitting in Dynamical Systems: A Practical Introduction with Applications and Software
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The paper introduces a numerical method to estimate parameters in systems of one-dimensional partial differential algebraic equations. Proceeding from given experimental data, i.e., observation times and measurements, the minimum least-squares distance of measured data from a fitting criterion is computed, which depends on the solution of the dynamical system. We present a typical black box approach that is easily implemented proceeding from some standard numerical analysis tools. Main emphasis of the paper is to present a couple of practical applications from industry and academia, to give an impression on the complexity of real-life systems of partial differential equations. The domains of application are pharmaceutics, geology, mechanical engineering, chemical engineering, food engineering, and electrical engineering.