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stat.ME2025
A Statistician's Overview of Physics-Informed Neural Networks for Spatio-Temporal Data
Christopher K. Wikle, Joshua North, Giri Gopalan +1
The recent success of deep neural network models with physical constraints (so-called, Physics-Informed Neural Networks, PINNs) has led to renewed interest in the incorporation of…
stat.ML2025
Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks
Rachel Longjohn, Giri Gopalan, Emily Casleton
Modern artificial intelligence is supported by machine learning models (e.g., foundation models) that are pretrained on a massive data corpus and then adapted to solve a variety of…
quant-ph2025
Length scale estimation of excited quantum oscillators
Tyler Volkoff, Giri Gopalan
Massive quantum oscillators are finding increasing applications in proposals for high-precision quantum sensors and interferometric detection of weak forces. Although optimal estim…