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Differential equation models are crucial to scientific processes across many disciplines, and the values of model parameters are important for analyzing the behaviour of solutions. Identifying these values is known as a parameter estimation, a type of inverse problem, which has applications in areas that include industry, finance and biomedicine. A parameter is called globally identifiable if its value can be uniquely determined from the input and output functions. Checking the global identifiability of model parameters is a useful tool when exploring the well-posedness of a given model. This problem has been intensively studied for ordinary differential equation models, where theory, several efficient algorithms and software packages have been developed. A comprehensive theory for PDEs has hitherto not been developed due to the complexity of initial and boundary conditions. Here, we provide theory and algorithms, based on differential algebra, for testing identifiability of polynomial PDE models. We showcase this approach on PDE models arising in the sciences. 

Dynamical systems are commonly used to represent real-world processes. Model reduction techniques are among the core tools for studying dynamical systems models, they allow to reduce the study of a model to a simpler one. In this poster, we present an algorithm for computing exact nonlinear reductions, that is, a set of new rational function macro-variables which satisfy a self-consistent ODE system with the dynamics defined by algebraic functions. We report reductions found by the algorithm in models from the literature.