The paper reformulates ODE parameter estimation as a derivative-matching least-squares problem and solves it with Gauss-Newton and gradient descent, showing better recovery than a bound-constrained NLS on three synthetic examples.
Osborne, and Tania Prvan
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Systems of ODEs Parameters Estimation by Using Stochastic Newton-Raphson and Gradient Descent Methods
The paper reformulates ODE parameter estimation as a derivative-matching least-squares problem and solves it with Gauss-Newton and gradient descent, showing better recovery than a bound-constrained NLS on three synthetic examples.