Conservative data-driven finite element formulation (2506.18206v1)

Abstract: This paper presents a new data-driven finite element framework derived with mixed finite element formulation. The standard approach to diffusion problems requires the solution of the mathematical equations that describe both the conservation law and the constitutive relations, where the latter is traditionally obtained after fitting experimental data to simplified material models. To exploit all available information and avoid bias in the material model, we follow a data-driven approach. While the conservation laws and boundary conditions are satisfied by means of the finite element method, the experimental data is used directly in the numerical simulations, avoiding the need of fitting material model parameters. In order to satisfy the conservation law a priori in the strong sense, we introduce a mixed finite element formulation. This relaxes the regularity requirements on approximation spaces while enforcing continuity of the normal flux component across all of the inner boundaries. This weaker mixed formulation provides a posteriori error indicators tailored for this data-driven approach, enabling adaptive hp-refinement. The relaxed regularity of the approximation spaces makes it easier to observe how the variation in the datasets results in the non-uniqueness of the solution, which can be quantified to predict the uncertainty of the results. The capabilities of the formulation are demonstrated in an example of the nonlinear heat transfer in nuclear graphite using synthetically generated material datasets.