313 citations · 857 across the 12 of their papers we have counts for
12 papers
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…
Kinetics Parameter Optimization via Neural Ordinary Differential Equations
Xingyu Su, Weiqi Ji, Jian An +3
Chemical kinetics mechanisms are essential for understanding, analyzing, and simulating complex combustion phenomena. In this study, a Neural Ordinary Differential Equation (Neural…
KiNet: A Deep Neural Network Representation of Chemical Kinetics
Weiqi Ji, Sili Deng
Deep learning is a potential approach to automatically develop kinetic models from experimental data. We propose a deep neural network model of KiNet to represent chemical kinetics…
Neural Differential Equations for Inverse Modeling in Model Combustors
Xingyu Su, Weiqi Ji, Long Zhang +3
Monitoring the dynamics processes in combustors is crucial for safe and efficient operations. However, in practice, only limited data can be obtained due to limitations in the meas…
Arrhenius.jl: A Differentiable Combustion SimulationPackage
Weiqi Ji, Xingyu Su, Bin Pang +6
Combustion kinetic modeling is an integral part of combustion simulation, and extensive studies have been devoted to developing both high fidelity and computationally affordable mo…
Autonomous Kinetic Modeling of Biomass Pyrolysis using Chemical Reaction Neural Networks
Weiqi Ji, Franz Richter, Michael J. Gollner +1
Modeling the burning processes of biomass such as wood, grass, and crops is crucial for the modeling and prediction of wildland and urban fire behavior. Despite its importance, the…
Machine Learning Approaches to Learn HyChem Models
Weiqi Ji, Julian Zanders, Ji-Woong Park +1
The HyChem approach has recently been proposed for modeling high-temperature combustion of real, multi-component fuels. The approach combines lumped reaction steps for fuel thermal…
Inference of cell dynamics on perturbation data using adjoint sensitivity
Weiqi Ji, Bo Yuan, Ciyue Shen +3
Data-driven dynamic models of cell biology can be used to predict cell response to unseen perturbations. Recent work (CellBox) had demonstrated the derivation of interpretable mode…
Stiff Neural Ordinary Differential Equations
Suyong Kim, Weiqi Ji, Sili Deng +2
Neural Ordinary Differential Equations (ODE) are a promising approach to learn dynamic models from time-series data in science and engineering applications. This work aims at learn…
Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics
Weiqi Ji, Weilun Qiu, Zhiyu Shi +2
Recently developed physics-informed neural network (PINN) has achieved success in many science and engineering disciplines by encoding physics laws into the loss functions of the n…
Autonomous Discovery of Unknown Reaction Pathways from Data by Chemical Reaction Neural Network
Weiqi Ji, Sili Deng
Chemical reactions occur in energy, environmental, biological, and many other natural systems, and the inference of the reaction networks is essential to understand and design the…
Kinetic Similarity between Extinction Strain Rate and Laminar Flame Speed
Weiqi Ji, Tianwei Yang, Zhuyin Ren +1
Extinction strain rate (ESR) and laminar flame speed (LFS) are fundamental properties of a fuel/air mixture that are often utilized as scaling parameters in turbulent combustion. W…