32 citations · 32 across the 4 of their papers we have counts for
5 papers
Fiber Bundle Morphisms as a Framework for Modeling Many-to-Many Maps
Elizabeth Coda, Nico Courts, Colby Wight +6
While it is not generally reflected in the `nice' datasets used for benchmarking machine learning algorithms, the real-world is full of processes that would be best described as ma…
Differential Property Prediction: A Machine Learning Approach to Experimental Design in Advanced Manufacturing
Loc Truong, WoongJo Choi, Colby Wight +4
Advanced manufacturing techniques have enabled the production of materials with state-of-the-art properties. In many cases however, the development of physics-based models of these…
A Topological-Framework to Improve Analysis of Machine Learning Model Performance
Henry Kvinge, Colby Wight, Sarah Akers +7
As both machine learning models and the datasets on which they are evaluated have grown in size and complexity, the practice of using a few summary statistics to understand model p…
Constrained Block Nonlinear Neural Dynamical Models
Elliott Skomski, Soumya Vasisht, Colby Wight +3
Neural network modules conditioned by known priors can be effectively trained and combined to represent systems with nonlinear dynamics. This work explores a novel formulation for…
Solving Allen-Cahn and Cahn-Hilliard Equations using the Adaptive Physics Informed Neural Networks
Colby L. Wight, Jia Zhao
Phase field models, in particular, the Allen-Cahn type and Cahn-Hilliard type equations, have been widely used to investigate interfacial dynamic problems. Designing accurate, effi…