On Parameters of Nonlinear Scalar Dynamics from Video: Invariants, Calibration, and Identifiability
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Computer Science > Machine Learning
Title:On Parameters of Nonlinear Scalar Dynamics from Video: Invariants, Calibration, and Identifiability
Abstract:Physical parameter estimation from video aims to recover the parameters of a known family of governing dynamical equations from pixel observations. Existing identifiability theory for this setting has focused on linear time-invariant (LTI) second-order systems, leaving open what can be identified for nonlinear scalar dynamics. We develop an identifiability theory for nonlinear scalar second-order ODEs, organized by how their velocity dependence interacts with changes of the learned state coordinate. Under a shared non-collapsed state map and explicit same-state velocity-coverage conditions, we show that parameter identifiability depends on the ODE family: some parameters are uniquely identifiable, while in other families only invariant parameter combinations are identifiable or external physical calibration is required. For laws that are at most linear in velocity, compatibility forces affine coordinate alignment, yielding explicit parameter relations, invariants, and calibration conditions. This affine conclusion extends to broader finite velocity-feature families when coordinate curvature can be separated from the declared velocity dependence. For families admitting a squared-velocity term, nonlinear coordinate ambiguity can remain; a law-derived normalization instead enables affine comparison between canonical laws. Experiments on synthetic systems and real pendulum and free-fall videos support the predicted parameter relations, coverage effects, and calibration requirements.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2609.38877 [cs.LG] |
| (or arXiv:2609.38877v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2609.38877
arXiv-issued DOI via DataCite (pending registration)
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