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Abstract: We describe an analytic method for the computation of equilibrium shapes for two-dimensional vesicles characterized by a Helfrich elastic energy. We derive boundary value problems and solve them analytically in terms of elliptic functions and elliptic integrals. We derive solutions by prescribing length and area, or displacements and angle boundary conditions. The solutions are compared to solutions obtained by a boundary integral equation-based numerical scheme. Our method enables the identification of different configurations of deformable vesicles and accurate calculation of their shape, bending moments, tension, and the pressure jump across the vesicle membrane. Furthermore, we perform numerical experiments that indicate that all these configurations are stable minima. [Copyright &y& Elsevier]

• Geometry-driven tensorization coupled with the tensor-train decomposition yields a hierarchical compression scheme. • A new streaming data compression scheme, in which batches of simulation data can be efficiently incorporated to a compressed representation as soon as they are generated. • Demonstration of high compression ratios (1 K-10 M fold reductions) of discrete element vehicle-over-soil simulation dataset. We propose a framework for discrete scientific data compression based on the tensor-train (TT) decomposition. Our approach is tailored to handle unstructured output data from discrete element method (DEM) simulations, demonstrating its effectiveness in compressing both raw (e.g. particle position and velocity) and derived (e.g. stress and strain) datasets. We show that geometry-driven "tensorization" coupled with the TT decomposition (known as quantized TT) yields a hierarchical compression scheme, achieving high compression ratios for key variables in these DEM datasets. [ABSTRACT FROM AUTHOR]