23 citations · 83 across the 26 of their papers we have counts for
28 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…
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…
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…
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…
Detection of False Data Injection Attacks (FDIA) on Power Dynamical Systems With a State Prediction Method
Abhijeet Sahu, Truc Nguyen, Kejun Chen +2
With the deeper penetration of inverter-based resources in power systems, false data injection attacks (FDIA) are a growing cyber-security concern. They have the potential to disru…
Discovery of False Data Injection Schemes on Frequency Controllers with Reinforcement Learning
Romesh Prasad, Malik Hassanaly, Xiangyu Zhang +1
While inverter-based distributed energy resources (DERs) play a crucial role in integrating renewable energy into the power system, they concurrently diminish the grid's system ine…
SUNDIALS Time Integrators for Exascale Applications with Many Independent ODE Systems
Cody J. Balos, Marc Day, Lucas Esclapez +8
Many complex systems can be accurately modeled as a set of coupled time-dependent partial differential equations (PDEs). However, solving such equations can be prohibitively expens…
Symbolic construction of the chemical Jacobian of quasi-steady state (QSS) chemistries for Exascale computing platforms
Malik Hassanaly, Nicholas T. Wimer, Anne Felden +5
The Quasi-Steady State Approximation (QSSA) can be an effective tool for reducing the size and stiffness of chemical mechanisms for implementation in computational reacting flow so…
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,…
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…
PINN surrogate of Li-ion battery models for parameter inference. Part II: Regularization and application of the pseudo-2D model
Malik Hassanaly, Peter J. Weddle, Ryan N. King +6
Bayesian parameter inference is useful to improve Li-ion battery diagnostics and can help formulate battery aging models. However, it is computationally intensive and cannot be eas…
PINN surrogate of Li-ion battery models for parameter inference. Part I: Implementation and multi-fidelity hierarchies for the single-particle model
Malik Hassanaly, Peter J. Weddle, Ryan N. King +6
To plan and optimize energy storage demands that account for Li-ion battery aging dynamics, techniques need to be developed to diagnose battery internal states accurately and rapid…
Ensemble flow reconstruction in the atmospheric boundary layer from spatially limited measurements through latent diffusion models
Alex Rybchuk, Malik Hassanaly, Nicholas Hamilton +3
Due to costs and practical constraints, field campaigns in the atmospheric boundary layer typically only measure a fraction of the atmospheric volume of interest. Machine learning…
Bi-fidelity Modeling of Uncertain and Partially Unknown Systems using DeepONets
Subhayan De, Matthew Reynolds, Malik Hassanaly +2
Recent advances in modeling large-scale complex physical systems have shifted research focuses towards data-driven techniques. However, generating datasets by simulating complex sy…
GANISP: a GAN-assisted Importance SPlitting Probability Estimator
Malik Hassanaly, Andrew Glaws, Ryan N. King
Designing manufacturing processes with high yield and strong reliability relies on effective methods for rare event estimation. Genealogical importance splitting reduces the varian…
Uniform-in-Phase-Space Data Selection with Iterative Normalizing Flows
Malik Hassanaly, Bruce A. Perry, Michael E. Mueller +1
Improvements in computational and experimental capabilities are rapidly increasing the amount of scientific data that is routinely generated. In applications that are constrained b…
Adversarial sampling of unknown and high-dimensional conditional distributions
Malik Hassanaly, Andrew Glaws, Karen Stengel +1
Many engineering problems require the prediction of realization-to-realization variability or a refined description of modeled quantities. In that case, it is necessary to sample e…
Surface chemistry models for GaAs epitaxial growth and hydride cracking using reacting flow simulations
Malik Hassanaly, Hariswaran Sitaraman, Kevin L. Schulte +5
Hydride Vapor Phase Epitaxy (HVPE) is a promising technology that can aid in the cost reduction of III-V materials and devices manufacturing, particularly high-efficiency solar cel…
Data-driven Analysis of Relight variability of Jet Fuels induced by Turbulence
Malik Hassanaly, Yihao Tang, Shivam Barwey +1
For safety purposes, reliable reignition of aircraft engines in the event of flame blow-out is a critical requirement. Typically, an external ignition source in the form of a spark…
A self-similarity principle for the computation of rare event probability
Malik Hassanaly, Venkat Raman
The probability of rare and extreme events is an important quantity for design purposes. However, computing the probability of rare events can be expensive because only a few event…
Lyapunov spectrum of forced homogeneous isotropic turbulent flows
Malik Hassanaly, Venkat Raman
In order to better understand deviations from equilibrium in turbulent flows, it is meaningful to characterize the dynamics rather than the statistics of turbulence. To this end, t…
Using machine learning to construct velocity fields from OH-PLIF images
Shivam Barwey, Malik Hassanaly, Venkat Raman +1
This work utilizes data-driven methods to morph a series of time-resolved experimental OH-PLIF images into corresponding three-component planar PIV fields in the closed domain of a…
Experimental Data Based Reduced Order Model for Analysis and Prediction of Flame Transition in Gas Turbine Combustors
Shivam Barwey, Malik Hassanaly, Qiang An +2
In lean premixed combustors, flame stabilization is an important operational concern that can affect efficiency, robustness and pollutant formation. The focus of this paper is on f…
Emerging Trends in Numerical Simulations of Combustion Systems
Venkat Raman, Malik Hassanaly
Numerical simulations have played a vital role in the design of modern combustion systems. Over the last two decades, the focus of research has been on the development of the large…