Our recent work on linear and affine dynamical systems has laid out a general framework for inferring the parameters of a differential equation model from a discrete set of data points collected from a system being modeled. It introduced a new class of inverse problems where qualitative information about the parameters and the associated dynamics of the system is determined for regions of the data space, rather than just for isolated experiments. Rigorous mathematical results have justified this approach and have identified common features that arise for certain classes of integrable models. In this work we present a thorough numerical investigation that shows that several of these core features extend to a paradigmatic linearinparameters model, the Lotka–Volterra (LV) system, which we consider in the conservative case as well as under the addition of terms that perturb the system away from this regime. A central construct for this analysis is a concise representation of parameter and dynamical features in the data space that we call the
 Award ID(s):
 1951095
 NSFPAR ID:
 10416185
 Publisher / Repository:
 IOP Publishing
 Date Published:
 Journal Name:
 Inverse Problems
 Volume:
 39
 Issue:
 7
 ISSN:
 02665611
 Page Range / eLocation ID:
 Article No. 075002
 Format(s):
 Medium: X
 Sponsoring Org:
 National Science Foundation
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