3 papers
cond-mat.mtrl-sci2026
Mapping Microstructure: Manifold Construction for Accelerated Materials Exploration
Simon A. Mason, Megna N. Shah, Jeffrey P. Simmons +2
Accelerating materials development requires quantitative linkages between processing, microstructure, and properties. In this work, we introduce a framework for mapping microstruct…
cs.LG2025
Training Variation of Physically-Informed Deep Learning Models
Ashley Lenau, Dennis Dimiduk, Stephen R. Niezgoda
A successful deep learning network is highly dependent not only on the training dataset, but the training algorithm used to condition the network for a given task. The loss functio…
cond-mat.mtrl-sci2024
Importance of hyper-parameter optimization during training of physics-informed deep learning networks
Ashley Lenau, Dennis M. Dimiduk, Stephen R. Niezgoda
Incorporating scientific knowledge into deep learning (DL) models for materials-based simulations can constrain the network's predictions to be within the boundaries of the materia…