Sumários
Model Implementation and Numerical Model Identification
13 Maio 2026, 13:30 • Rafael Neto Henriques
This session continued to provide hands-on support for students regarding the practical implementation of their selected physiological models, with a persistent focus on resolving programming challenges and numerical troubleshooting. Building on the concepts of model identification recently covered in theoretical lectures, instructors also guided students on how to practically apply these principles. Specifically, the class discussed strategies for performing numerical model identification within their MATLAB or Python projects, helping students bridge the gap between theoretical identifiability and practical parameter estimation using simulated data.
Model identification part 2 - Errors Sources and intro to a priori identification
13 Maio 2026, 10:00 • Rafael Neto Henriques
Continuing the module on model identification, this theoretical class began by examining the various sources of error that can impact the estimation of model parameters, including errors in test signals, system disturbances, structural model errors, and measurement errors. Despite these practical challenges, the session clarified that theoretical model identification evaluates a system independent of these imperfections. Specifically, it seeks to answer whether it is mathematically possible to estimate all unknown parameters of a given parametric model under the assumption of perfect, error-free data. Building on this premise, the class formally introduced the concept of a priori identifiability alongside the concept of data observational parameters. To provide a rigorous mathematical framework for these concepts, the session detailed a formalism for a priori identification using the Laplace transform. The class concluded with a practical demonstration of this mathematical approach, walking students through a step-by-step example of a fully identifiable physiological model to solidify the theory.
Model Implementation and Numerical Model Identification
13 Maio 2026, 08:30 • Rafael Neto Henriques
This session continued to provide hands-on support for students regarding the practical implementation of their selected physiological models, with a persistent focus on resolving programming challenges and numerical troubleshooting. Building on the concepts of model identification recently covered in theoretical lectures, instructors also guided students on how to practically apply these principles. Specifically, the class discussed strategies for performing numerical model identification within their MATLAB or Python projects, helping students bridge the gap between theoretical identifiability and practical parameter estimation using simulated data.
Model Identification part 1 - Test Signals
12 Maio 2026, 11:00 • Rafael Neto Henriques
Having concluded the exploration of specific data and structural models, this theoretical class initiated a deep dive into Model Identification. The session began by reviewing the core concept of model identification, formally defining it as the process of determining whether a model's specific parameters can be reliably and accurately estimated from a given set of acquired data. Recognizing that the natural baseline dynamics of a physiological system are often insufficient for complete parameter estimation, the class introduced the concept and application of experimental test signals. The general criteria for designing appropriate physiological perturbations were discussed, followed by a detailed examination of three specific categories of test signals: