149 citations · 170 across the 6 of their papers we have counts for
6 papers
Machine Learning-Driven Chemical Reactor Network Modeling of the Sandia-D Flame
Nicolas J. Tricard, Benjamin C. Koenig, Sili Deng
Turbulent combustion simulations are crucial for many scientific and engineering systems. However, the high cost to fully resolve the complex multiscale and multiphysics behavior m…
Learning continuous state of charge dependent thermal decomposition kinetics for Li-ion cathodes using Kolmogorov-Arnold Chemical Reaction Neural Networks (KA-CRNNs)
Benjamin C. Koenig, Sili Deng
Thermal runaway in lithium-ion batteries is strongly influenced by the state of charge (SOC). Existing predictive models typically infer scalar kinetic parameters at a full SOC or…
Kolmogorov-Arnold Chemical Reaction Neural Networks for learning pressure-dependent kinetic rate laws
Benjamin C. Koenig, Sili Deng
Chemical Reaction Neural Networks (CRNNs) have emerged as an interpretable machine learning framework for discovering reaction kinetics directly from data, while strictly adhering…
ChemKANs for Combustion Chemistry Modeling and Acceleration
Benjamin C. Koenig, Suyong Kim, Sili Deng
Efficient chemical kinetic model inference and application in combustion are challenging due to large ODE systems and widely separated time scales. Machine learning techniques have…
LeanKAN: A Parameter-Lean Kolmogorov-Arnold Network Layer with Improved Memory Efficiency and Convergence Behavior
Benjamin C. Koenig, Suyong Kim, Sili Deng
The recently proposed Kolmogorov-Arnold network (KAN) is a promising alternative to multi-layer perceptrons (MLPs) for data-driven modeling. While original KAN layers were only cap…
KAN-ODEs: Kolmogorov-Arnold Network Ordinary Differential Equations for Learning Dynamical Systems and Hidden Physics
Benjamin C. Koenig, Suyong Kim, Sili Deng
Kolmogorov-Arnold networks (KANs) as an alternative to multi-layer perceptrons (MLPs) are a recent development demonstrating strong potential for data-driven modeling. This work ap…