48 citations · 52 across the 11 of their papers we have counts for
4 papers · 1 filter
Formally Exploring Time-Series Anomaly Detection Evaluation Metrics
Dennis Wagner, Arjun Nair, Billy Joe Franks +24
Undetected anomalies in time series can trigger catastrophic failures in safety-critical systems, such as chemical plant explosions or power grid outages. Although many detection m…
DiffStyleTS: Diffusion Model for Style Transfer in Time Series
Mayank Nagda, Phil Ostheimer, Justus Arweiler +13
Style transfer combines the content of one signal with the style of another. It supports applications such as data augmentation and scenario simulation, helping machine learning mo…
PIANO: Physics Informed Autoregressive Network
Mayank Nagda, Jephte Abijuru, Phil Ostheimer +2
Solving time-dependent partial differential equations (PDEs) is fundamental to modeling critical phenomena across science and engineering. Physics-Informed Neural Networks (PINNs)…
Sparse Data Diffusion for Scientific Simulations in Biology and Physics
Phil Ostheimer, Mayank Nagda, Andriy Balinskyy +5
Sparse data is fundamental to scientific simulations in biology and physics, from single-cell gene expression to particle calorimetry, where exact zeros encode physical absence rat…