Recent works have demonstrated the viability of convolutional neural networks (CNN) for
capturing the highly non-linear microstructure-property linkages in high contrast composite …

Conditional microstructure generation tools offer an important, inexpensive pathway to
constructing statistically diverse datasets for Integrated Computational Materials …

Recent advances in operator learning theory have improved our knowledge about learning
maps between infinite dimensional spaces. However, for large-scale engineering problems …

The availability of large, diverse datasets has enabled transformative advances in a wide
variety of technical fields by unlocking data scientific and machine learning techniques. In …

This work introduces a lean CNN (convolutional neural network) framework, with a
drastically reduced number of fittable parameters (< 81K) compared to the benchmarks in …

We present an iterative framework to improve the amortized approximations of posterior
distributions in the context of Bayesian inverse problems, which is inspired by loop-unrolled …

We present a comprehensive benchmarking framework for evaluating machine-learning
approaches applied to phase-field problems. This framework focuses on four key analysis …

Process-structure-properties linkages play a major role in materials and process
engineering. Nowadays, such linkages are often established on the basis of experimental …