dos Santos, Ketson R. M.Giovanis, Dimitris G.Kontolati, KatianaLoukrezis, DimitriosShields, Michael D.2022-04-252022-04-252022-04-252022-04-1110.1002/nme.6977https://infoscience.epfl.ch/handle/20.500.14299/187315WOS:000780012900001A novel surrogate model based on the Grassmannian diffusion maps (GDMaps) and utilizing geometric harmonics (GH) is developed for predicting the response of complex physical phenomena. The method utilizes GDMaps to obtain a low-dimensional representation of the underlying behavior of physical/mathematical systems with respect to uncertain input parameters. Using this representation, GH, an out-of-sample extension technique, is employed to create a global map from the input parameter space to a Grassmannian diffusion manifold. GH is further employed to locally map points on the diffusion manifold onto the tangent space of a Grassmann manifold. The exponential map is then used to project the points in the tangent space onto the Grassmann manifold, where reconstruction of the full solution is performed. The performance of the proposed surrogate model is verified with three examples. The first problem is a toy example used to illustrate the technique. In the second example, errors associated with the various mappings are assessed by studying response predictions of the electric potential of a dielectric cylinder in a homogeneous electric field. The last example applies the method for uncertainty prediction in the strain field evolution in a model amorphous material using the shear transformation zone theory of plasticity.Engineering, MultidisciplinaryMathematics, Interdisciplinary ApplicationsEngineeringMathematicsdiffusion mapsgeometric harmonicsgrassmann manifoldmanifold learningsurrogate modelingdimensionality reductionuncertaintyalgorithmssystemstooltext::journal::journal article::research article