Distribution-based microstructure descriptors recover a two-dimensional latent manifold aligned with processing parameters in spinodal decomposition simulations, enabling invertible mappings.
Multidimensional scaling
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
A gradient manifold optimization method simultaneously learns a dimension reduction mapping and clusters the projected data under a GMM, reporting better results than standard clustering on MNIST.
citing papers explorer
-
Mapping Microstructure: Manifold Construction for Accelerated Materials Exploration
Distribution-based microstructure descriptors recover a two-dimensional latent manifold aligned with processing parameters in spinodal decomposition simulations, enabling invertible mappings.
-
Joint Representation Learning and Clustering via Gradient-Based Manifold Optimization
A gradient manifold optimization method simultaneously learns a dimension reduction mapping and clusters the projected data under a GMM, reporting better results than standard clustering on MNIST.