6 papers
Neural posterior estimation for scalable and accurate inverse parameter inference in Li-ion batteries
Malik Hassanaly, Corey R. Randall, Peter J. Weddle +4
Diagnosing the internal state of Li-ion batteries is critical for battery research, operation of real-world systems, and prognostic evaluation of remaining lifetime. By using physi…
Adversarial Multi-Agent Reinforcement Learning for Proactive False Data Injection Detection
Kejun Chen, Truc Nguyen, Abhijeet Sahu +1
Smart inverters are instrumental in the integration of distributed energy resources into the electric grid. Such inverters rely on communication layers for continuous control and m…
Swift Hydra: Self-Reinforcing Generative Framework for Anomaly Detection with Multiple Mamba Models
Nguyen Do, Truc Nguyen, Malik Hassanaly +3
Despite a plethora of anomaly detection models developed over the years, their ability to generalize to unseen anomalies remains an issue, particularly in critical systems. This pa…
Bayesian calibration of bubble size dynamics applied to CO2 gas fermenters
Malik Hassanaly, John M. Parra-Alvarez, Mohammad J. Rahimi +2
To accelerate the scale-up of gaseous CO2 fermentation reactors, computational models need to predict gas-to-liquid mass transfer which requires capturing the bubble size dynamics,…
Continual Adversarial Reinforcement Learning (CARL) of False Data Injection detection: forgetting and explainability
Pooja Aslami, Kejun Chen, Timothy M. Hansen +1
False data injection attacks (FDIAs) on smart inverters are a growing concern linked to increased renewable energy production. While data-based FDIA detection methods are also acti…
A Priori Uncertainty Quantification of Reacting Turbulence Closure Models using Bayesian Neural Networks
Graham Pash, Malik Hassanaly, Shashank Yellapantula
While many physics-based closure model forms have been posited for the sub-filter scale (SFS) in large eddy simulation (LES), vast amounts of data available from direct numerical s…