Discovered the Jitter Paradox in physics-informed AI, accepted at an IEEE conference
Physics-informed neural networks learn solutions that obey physical laws, and newer architectures called Kolmogorov-Arnold Networks often solve equations more accurately. Nishanth asked a sharper question: are those solutions good enough to rediscover the underlying equation? Comparing both architectures on the heat equation under noise, he found a Jitter Paradox. The more accurate networks produced rougher derivatives that corrupted symbolic equation discovery, while simpler networks recovered the physics more reliably. He also proposed a new metric, the Bias Inheritance Ratio, to measure how model errors leak into discovered equations.
An 11th grader at Delhi Public School Bangalore North who taught himself real analysis and university mathematics because his school did not offer them.
First author of a study that named and quantified the Jitter Paradox in physics-informed neural networks, accepted at an IEEE conference with minor revisions.