Physics-informed neural networks for sparse neutron source reconstruction

By Adam Glick, Miles O'Brien, and Mustapha Saad
Backed by Harmilee Cousin III, Nucleation Capital, Christina Glick, Matt M., Michael S Pukish, Nathaniel Thorne, Rudolf Kardos, Andrew Opalewski, Andrea Garecht, Moshe Cohen, and 8 other backers
Glick Independent Physics Lab
Birmingham, Alabama
Computer SciencePhysics
DOI: 10.18258/82462
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Lab Notes

Generate High-Fidelity Simulated Neutron Detector Data
January 22, 2026
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Project Update - Materials Cost Adjustment
January 15, 2026
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Background and Methods
December 24, 2025
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PINN Reconstruction Achieves >95% Speed Improvement Over...
December 15, 2025
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Expanded neural network's capacity
December 8, 2025
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Proof of concept for the PINN verified
November 18, 2025
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