8 citations · 19 across the 32 of their papers we have counts for
7 papers · 1 filter
Staying on the Attractor: Supervising Neural Surrogates of 3D Turbulence Where They Leave It
Yilong Dai, Shaswata Mitra, Raj Patel +7
Neural surrogates are trained to predict 3D turbulent flows in place of direct numerical simulation (DNS). For chaotic flows, the goal is short-term pointwise accuracy followed by…
RAAD-LLM: Adaptive Anomaly Detection Using LLMs and RAG Integration
Alicia Russell-Gilbert, Sudip Mittal, Shahram Rahimi +4
Anomaly detection in complex industrial environments poses unique challenges, particularly in contexts characterized by data sparsity and evolving operational conditions. Predictiv…
Multivariate Data Augmentation for Predictive Maintenance using Diffusion
Andrew Thompson, Alexander Sommers, Alicia Russell-Gilbert +7
Predictive maintenance has been used to optimize system repairs in the industrial, medical, and financial domains. This technique relies on the consistent ability to detect and pre…
AAD-LLM: Adaptive Anomaly Detection Using Large Language Models
Alicia Russell-Gilbert, Alexander Sommers, Andrew Thompson +7
For data-constrained, complex and dynamic industrial environments, there is a critical need for transferable and multimodal methodologies to enhance anomaly detection and therefore…
Explainable Anomaly Detection: Counterfactual driven What-If Analysis
Logan Cummins, Alexander Sommers, Sudip Mittal +4
There exists three main areas of study inside of the field of predictive maintenance: anomaly detection, fault diagnosis, and remaining useful life prediction. Notably, anomaly det…
A Survey of Transformer Enabled Time Series Synthesis
Alexander Sommers, Logan Cummins, Sudip Mittal +4
Generative AI has received much attention in the image and language domains, with the transformer neural network continuing to dominate the state of the art. Application of these m…
Generating Synthetic Time Series Data for Cyber-Physical Systems
Alexander Sommers, Somayeh Bakhtiari Ramezani, Logan Cummins +4
Data augmentation is an important facilitator of deep learning applications in the time series domain. A gap is identified in the literature, demonstrating sparse exploration of th…