Handling geometrical variability in nonlinear reduced order modeling through Continuous Geometry-Aware DL-ROMs

Deep Learning-based Reduced Order Models (DL-ROMs) provide nowadays a well-established class of accurate surrogate models for complex physical systems described by parameterised PDEs, by nonlinearly compressing the solution manifold into a handful of latent coordinates. Until now, design and application of DL-ROMs mainly focused on physically parameterised problems. Within this work, we provide a novel extension of these architectures to problems featuring geometrical variability and parameterised domains, namely, we propose Continuous Geometry-Aware DL-ROMs (CGA-DL-ROMs). In particular, the space-continuous nature of the proposed architecture matches the need to deal with multi-resolution datasets, which are quite common in the case of geometrically parameterised problems. Moreover, CGA-DL-ROMs are endowed with a strong inductive bias that makes them aware of geometrical parametrizations, thus enhancing both the compression capability and the overall performance of the architecture. Within this work, we justify our findings through a thorough theoretical analysis, and we practically validate our claims by means of a series of numerical tests encompassing physically-and-geometrically parameterised PDEs, ranging from the unsteady Navier–Stokes equations for fluid dynamics to advection–diffusion–reaction equations for mathematical biology. • Dimensionality reduction framework for geometrically parameterised problems. • Exploiting space-continuous geometry-aware basis functions. • Theoretical and numerical analysis of the framework’s compression capabilities. • Application to benchmarks related to fluid dynamics and mathematical biology.