Overview
Personal Profile
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Corey Randall specializes in multi-fidelity modeling and design optimization of electrochemical devices, focusing on reduced-order and continuum-level models. Currently, Corey is working on predicting and optimizing the performance of direct-recycled cathode materials, including blends, and developing early life prediction models for both electric vehicle and long-duration energy storage batteries.
Research Interests
Electrochemical energy storage
Physics-based modeling
Design optimization
Machine learning
Material/microstructure characterization
Education/Academic Qualification
PhD, Mechanical Engineering, Colorado School of Mines
Master, Mechanical Engineering, Colorado School of Mines
Bachelor, Chemical Engineering and Mathematics, Colorado State University
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Collaborations and Top Research Areas From the Past 5 Years
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Achieving High Rate Performance in Hybrid Pristine-Recycled Cathodes Using Model-Informed Electrode Designs: Article No. 148385
Randall, C., McKalip, N., Fink, K., Verma, A., Singh, A., Mallarapu, A., Weddle, P. & Colclasure, A., 2026, In: Electrochimica Acta. 555, 15 p.Research output: Contribution to journal › Article › peer-review
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PINN Surrogate of Li-Ion Battery Models for Parameter Inference, Part I: Implementation and Multi-Fidelity Hierarchies for the Single-Particle Model: Article No. 113103
Hassanaly, M., Weddle, P., King, R., De, S., Doostan, A., Randall, C., Dufek, E., Colclasure, A. & Smith, K., 2024, In: Journal of Energy Storage. 98, Part B, 13 p.Research output: Contribution to journal › Article › peer-review
26 Scopus Citations -
PINN Surrogate of Li-Ion Battery Models for Parameter Inference, Part II: Regularization and Application of the Pseudo-2D Model: Article No. 113104
Hassanaly, M., Weddle, P., King, R., De, S., Doostan, A., Randall, C., Dufek, E., Colclasure, A. & Smith, K., 2024, In: Journal of Energy Storage. 98, Part B, 17 p.Research output: Contribution to journal › Article › peer-review
23 Scopus Citations -
Rapid Inverse Parameter Inference Using Physics-Informed Neural Network
Hassanaly, M., Weddle, P., Randall, C., Dufek, E. & Smith, K., 2024, National Laboratory of the Rockies (NLR).Research output: NLR › Poster