1 citations · 2 across the 4 of their papers we have counts for
4 papers
Towards Autonomous Experimentation: Bayesian Optimization over Problem Formulation Space for Accelerated Alloy Development
Danial Khatamsaz, Joseph Wagner, Brent Vela +2
Accelerated discovery in materials science demands autonomous systems capable of dynamically formulating and solving design problems. In this work, we introduce a novel framework t…
Hierarchical Gaussian Process-Based Bayesian Optimization for Materials Discovery in High Entropy Alloy Spaces
Sk Md Ahnaf Akif Alvi, Jan Janssen, Danial Khatamsaz +3
Bayesian optimization (BO) is a powerful and data-efficient method for iterative materials discovery and design, particularly valuable when prior knowledge is limited, underlying f…
Supply Risk-Aware Alloy Discovery and Design
Mrinalini Mulukutla, Robert Robinson, Danial Khatamsaz +3
Materials design is a critical driver of innovation, yet overlooking the technological, economic, and environmental risks inherent in materials and their supply chains can lead to…
Label Propagation for Physics-Informed Neural Networks and Physics-Informed Gaussian Processes
Ming Zhong, Dehao Liu, Raymundo Arroyave +1
We present a series of empirical results of the application of semi-supervised label propagation techniques in training physics-informed machine learning methods. This includes sel…