14 citations · 20 across the 7 of their papers we have counts for
7 papers
Performance-driven Computational Design of Multi-terminal Compositionally Graded Alloy Structures using Graphs
Marshall D. Allen, Vahid Attari, Brent Vela +3
The spatial control of material placement afforded by metal additive manufacturing (AM) has enabled significant progress in the development and implementation of compositionally gr…
Visualizing High Entropy Alloy Spaces: Methods and Best Practices
Brent Vela, Trevor Hastings, Raymundo Arróyave
Multi-Principal Element Alloys (MPEAs) have emerged as an exciting area of research in materials science in the 2020s, owing to the vast potential for discovering alloys with uniqu…
Label Propagation Training Schemes for Physics-Informed Neural Networks and Gaussian Processes
Ming Zhong, Dehao Liu, Raymundo Arroyave +1
This paper proposes a semi-supervised methodology for training physics-informed machine learning methods. This includes self-training of physics-informed neural networks and physic…
High-throughput Alloy and Process Design for Metal Additive Manufacturing
Sofia Sheikh, Brent Vela, Pejman Honarmandi +6
Designing alloys for additive manufacturing (AM) presents significant opportunities. Still, the chemical composition and processing conditions required for printability (ie., their…
An Automated Fully-Computational Framework to Construct Printability Maps for Additively Manufactured Metal Alloys
Sofia Sheikh, Meelad Ranaiefar, Pejman Honarmandi +6
In additive manufacturing, the optimal processing conditions need to be determined to fabricate porosity-free parts. For this purpose, the design space for an arbitrary alloy needs…
Efficient Propagation of Uncertainty via Reordering Monte Carlo Samples
Danial Khatamsaz, Vahid Attari, Raymundo Arroyave +1
Uncertainty analysis in the outcomes of model predictions is a key element in decision-based material design to establish confidence in the models and evaluate the fidelity of mode…