PNNL

Ethan King is an applied mathematician with experience in control, optimization, modeling, and machine learning. He is interested in leveraging the tools and successes of data science to push the boundaries of complexity and scale possible within scientific computing. Within the domain of modeling, his interests are in methods to learn uncertain physics in complex systems from data, and to merge domain knowledge with machine learning for fast accurate predictions. Within optimization, his interests are in the use of differentiable programming to greatly increase the solution speed for complex and large scale problems.

PhD in applied mathematics, North Carolina State University

BS in biology and mathematics, University of Utah

2025

Ciesielski, D., Y. Li, S. Hu, E. King, J. Corbey, and P. Stinis. 2025. “Deep Operator Network Surrogate for Phase-Field Modeling of Metal Grain Growth during Solidification.” Computational Materials Science 246. https://doi.org/10.1016/j.commatsci.2024.113417.

2023

King, E., Y. Li, S. Hu, and E. Machorro. 2023. “Physics-Informed Machine-Learning Model of Temperature Evolution under Solid Phase Processes.” Computational Mechanics 72 (1): 125-136. https://doi.org/10.1007/s00466-023-02289-9.