Model-free data-driven computational mechanics enhanced by tensor voting

The data-driven computing paradigm initially introduced by Kirchdoerfer & Ortiz (2016) is extended incorporating locally linear tangent spaces into the data set. These are constructed means of tensor voting method Mordohai Medioni (2010) which improves learning underlying structure a Tensor an instance-based machine technique accumulates votes from nearest neighbors to build up second-order tensors encoding tangents and normals structure. here proposed plug-in for distance-minimizing as well entropy-maximizing schemes. Like its predecessor, resulting aims minimize suitably defined free energy over phase space subject compatibility equilibrium constraints. method's implementation straightforward numerically efficient since analysis performed in offline step. Selected numerical examples presented that establish higher-order convergence properties solvers enhanced ideal noisy sets.