Extends structural identifiability analysis to functional components of differential equation models and characterizes conditions for unique recovery using differential algebra techniques.
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5 Pith papers cite this work, alongside 16 external citations. Polarity classification is still indexing.
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Pith papers citing it
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representative citing papers
A kernel-based regularizer derived from a GP prior on the Neural ODE vector field at finite points is added to the variational objective, paired with multiple shooting to handle long irregular trajectories.
A sequential experimental design technique discriminates between model structures from symbolic regression to discover missing physics in process systems such as bioreactors.
A conceptual discussion clarifying the roles of aleatoric and epistemic uncertainty when modeling dynamical systems across ML tasks.
citing papers explorer
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Structural functional identifiability and model discovery in differential equation models
Extends structural identifiability analysis to functional components of differential equation models and characterizes conditions for unique recovery using differential algebra techniques.
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Function-Space Priors for Bayesian Neural ODEs with Application to Vessel Trajectory Prediction
A kernel-based regularizer derived from a GP prior on the Neural ODE vector field at finite points is added to the variational objective, paired with multiple shooting to handle long irregular trajectories.
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Experimental Design for Missing Physics
A sequential experimental design technique discriminates between model structures from symbolic regression to discover missing physics in process systems such as bioreactors.
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What Uncertainties Do We Need for Dynamical Systems?
A conceptual discussion clarifying the roles of aleatoric and epistemic uncertainty when modeling dynamical systems across ML tasks.
- Estimating Parameter Fields in Multi-Physics PDEs from Scarce Measurements