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Found 5 papers, 1 papers with code
Hybrid Square Neural ODE Causal Modeling
We encode this information in a causal loss that we combine with the standard predictive loss to arrive at a hybrid loss that biases our learning towards causally valid hybrid models.
Learning About Structural Errors in Models of Complex Dynamical Systems
Complex dynamical systems are notoriously difficult to model because some degrees of freedom (e. g., small scales) may be computationally unresolvable or are incompletely understood, yet they are dynamically important.
Learning Absorption Rates in Glucose-Insulin Dynamics from Meal Covariates
In this paper, we propose to learn the effects of macronutrition content from glucose-insulin data and meal covariates.
A Framework for Machine Learning of Model Error in Dynamical Systems
For ergodic continuous-time systems, we prove that both excess risk and generalization error are bounded above by terms that diminish with the square-root of T, the time-interval over which training data is specified.
Behavioral-clinical phenotyping with type 2 diabetes self-monitoring data
Materials and Methods: We used hierarchical clustering (HC) to identify groups of meals with similar nutrition and glycemic impact for 6 individuals with T2DM who collected self-monitoring data.
