4 papers
Data Field Theory: Theory and Applications of the Functional Renormalization Group for Signal Detection
Riccardo Finotello, Vincent Lahoche, Dine Ousmane Samary +1
We review the renormalization group framework for signal detection in high-dimensional data, tailored to the regime where the signal may be of extensive rank and does not separate…
Field Theory of Data: Anomaly Detection via the Functional Renormalization Group. The 2D Ising Model as a Benchmark
Riccardo Finotello, Vincent Lahoche, Parham Radpay +1
We establish a correspondence between anomaly detection in high-noise regimes and the renormalization group flow of non-equilibrium field theories. We provide a physical grounding…
Functional Renormalization for Signal Detection: Dimensional Analysis and Dimensional Phase Transition for Nearly Continuous Spectra Effective Field Theory
Riccardo Finotello, Vincent Lahoche, Dine Ousmane Samary
Signal detection in high dimensions is a critical challenge in data science. While standard methods based on random matrix theory provide sharp detection thresholds for finite-rank…
Learning Complex Physical Regimes via Coverage-oriented Uncertainty Quantification: An application to the Critical Heat Flux
Michele Cazzola, Alberto Ghione, Lucia Sargentini +2
A central challenge in scientific machine learning (ML) is the correct representation of physical systems governed by multi-regime behaviours. In these scenarios, standard data ana…