WENDy for Nonlinear-in-Parameters ODEs

• URL: http://arxiv.org/abs/2502.08881v3
• Date: Thu, 23 Oct 2025 10:25:21 GMT
• Title: WENDy for Nonlinear-in-Parameters ODEs
• Authors: Nic Rummel, Daniel A. Messenger, Stephen Becker, Vanja Dukic, David M. Bortz,
• Abstract summary: WENDy-MLE approximates a maximum likelihood estimator via local non- convergence optimization methods.<n>WENDy-MLE has better accuracy, a substantially larger domain of convergence, and often is faster than other weak form methods and the conventional output error least squares method.<n>We present extensive numerical results comparing our method, other weak form methods, and output error least squares on a suite of benchmark systems of ODEs.
• Score: 2.066079080612853
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The Weak-form Estimation of Non-linear Dynamics (WENDy) framework is a recently developed approach for parameter estimation and inference of systems of ordinary differential equations (ODEs). Prior work demonstrated WENDy to be robust, computationally efficient, and accurate, but only works for ODEs which are linear-in-parameters. In this work, we derive a novel extension to accommodate systems of a more general class of ODEs that are nonlinear-in-parameters. Our new WENDy-MLE algorithm approximates a maximum likelihood estimator via local non-convex optimization methods. This is made possible by the availability of analytic expressions for the likelihood function and its first and second order derivatives. WENDy-MLE has better accuracy, a substantially larger domain of convergence, and is often faster than other weak form methods and the conventional output error least squares method. Moreover, we extend the framework to accommodate data corrupted by multiplicative log-normal noise. The WENDy.jl algorithm is efficiently implemented in Julia. In order to demonstrate the practical benefits of our approach, we present extensive numerical results comparing our method, other weak form methods, and output error least squares on a suite of benchmark systems of ODEs in terms of accuracy, precision, bias, and coverage.

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