Praadhyumn Indaana, the 17-year-old Montvale, New Jersey student (Image Credit: Davidson Institute)
For 17-year-old Praadhyumn Indaana of Montvale, New Jersey, that challenge became the focus of an eight-month research project combining physics and artificial intelligence. Which named Indaana a 2026 Davidson Fellow, his physics-guided neural network reduced the average prediction error for critical heat flux from about 63% with conventional formulas to 5.5%, according to the Davidson Institute. The work earned him a $50,000 Davidson Fellows Scholarship and recognition for a project aimed at improving predictions of an important nuclear-reactor operating limit. The conventional formulas showed average errors of about 63%, while Indaana’s model reduced that figure to 5.5%, according to the Davidson Institute. On a fixed testing dataset, the model achieved an R² value of 0.986, a statistical measure indicating how closely the model’s predictions matched the observed data in that evaluation.
Because a conventional machine-learning model can struggle when it encounters situations for which there is little training data, this distinction was important.
Early versions of his models performed well in regions with large amounts of data but could produce physically unreasonable predictions when extrapolating into less represented high-quality-flow regions, according to Indaana. He addressed the problem by adjusting the physics-based regularisation according to how closely different empirical correlations agreed for individual samples. Indaana compared its performance with widely used correlations, which he says produced errors an order of magnitude larger.
A better understanding of how much heat a nuclear reactor can safely handle could have implications far beyond a single engineering calculation. He also added a penalty when predictions exceeded a known physical upper bound. The resulting model produced a major improvement in prediction accuracy.
The achievement does not mean that nuclear reactors can simply be operated closer to the heat limit. Reactor safety requirements remain stringent. Instead, more accurate prediction could help engineers understand the boundary more precisely and potentially reduce some of the conservatism built into reactor design and operation. Indaana completed the project over roughly eight months, working through literature review, data collection, feature engineering, model development, statistical evaluation and paper writing. He taught himself much of the thermohydraulics, physics-informed machine learning and statistics required for the work.