Benchmark computations for the polarization tensor characterization of small conducting objects

• Novel, accurate computational solutions to a series of polarizability tensor object characterisations. Application the Boundary Element Python Package (Bempp) and grid adaption characterization small objects. Benchmark computations interest electrical impedance tomography practitioners software developers. The characterisation low conducting inclusions in an otherwise uniform background from low-frequency field measurements has important applications medical imaging using as well geological resistivity tomography. It is known that such objects can be characterised by Póyla-Szegö (polarizability) tensor. Such characterisations have attracted they provide features machine learning classification algorithm alternative solution. However, able train algorithms, large dictionaries are required it essential accurate. In this work, we obtain numerical approximations coefficients, applying adaptive boundary element method. goal being sequence benchmark for coefficients allow other developers check accuracy their codes.