175 citations · 228 across the 4 of their papers we have counts for
5 papers
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…
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…
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…
The Universality Class of Nano-Crystal Plasticity: Self-Organization and Localization in Discrete Dislocation Dynamics
Hengxu Song, Dennis Dimiduk, Stefanos Papanikolaou
The universality class of the avalanche behavior in plastically deforming crystalline and amorphous systems has been commonly discussed, despite the fact that the microscopic defec…
Quasi-periodic events in crystal plasticity and the self-organized avalanche oscillator
Stefanos Papanikolaou, Dennis M. Dimiduk, Woosong Choi +4
When external stresses in a system - physical, social or virtual - are relieved through impulsive events, it is natural to focus on the attributes of these avalanches. However, dur…