A data-driven reduced-order model for stiff chemical kinetics using dynamics-informed training
A data-based reduced-order model (ROM) is developed to accelerate the time integration of
stiff chemically reacting systems by effectively removing the stiffness arising from a wide …
stiff chemically reacting systems by effectively removing the stiffness arising from a wide …
An integrated framework for accelerating reactive flow simulation using GPU and machine learning models
Recent progress in machine learning (ML) and high-performance computing (HPC) have
brought potentially game-changing opportunities in accelerating reactive flow simulations. In …
brought potentially game-changing opportunities in accelerating reactive flow simulations. In …
A co-kurtosis PCA based dimensionality reduction with nonlinear reconstruction using neural networks
For turbulent reacting flow systems, identification of low-dimensional representations of the
thermo-chemical state space is vitally important, primarily to significantly reduce the …
thermo-chemical state space is vitally important, primarily to significantly reduce the …
An asynchronous discontinuous Galerkin method for combustion simulations
AK Arumugam, K Aditya - arXiv preprint arXiv:2501.01747, 2025 - arxiv.org
The discontinuous Galerkin (DG) method has been widely considered in recent years to
develop scalable flow solvers for its ability to handle discontinuities, such as shocks and …
develop scalable flow solvers for its ability to handle discontinuities, such as shocks and …