14 citations · 22 across the 5 of their papers we have counts for
8 papers
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…
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…