The recently developed physics-informed neural network (PINN) has achieved success in
many science and engineering disciplines by encoding physics laws into the loss functions …

Emerging supercomputing systems utilize a combination of central processing units (CPUs)
and graphics processing units (GPUs) in an effort to reach exascale capabilities while …

In this study, the open-source Hypersonics Task-based Research (HTR) solver for
hypersonic aerothermodynamics is described. The physical formulation of the code includes …

This investigation outlines a data-assisted approach that employs random forest classifiers
for local and dynamic submodel assignment in turbulent-combustion simulations. This …

This work proposes a new machine learning (ML)-based paradigm aiming to enhance the
computational efficiency of non-equilibrium reacting flow simulations while ensuring …

Rotating detonation engines (RDEs) are pressure-gain combustion devices which allow for
a compact and mechanically simple engine design with improved thermodynamic efficiency …

In this paper, we develop a fully conservative, positivity-preserving, and entropy-bounded
discontinuous Galerkin scheme for simulating the multicomponent, chemically reacting …

To observe a Weyl point (WP) possessing the maximum charge number and to search for
single-pair WPs in realistic materials are two challenges in topology physics. Here, we have …

The dynamics and structure of detonations in stratified, product-gas diluted mixtures are
examined using high resolution two-dimensional simulations with detailed chemistry. A …

Jacobian clustering is proposed to reduce the computational cost associated with matrix
operations encountered in the Newton iteration in fully coupled, fully implicit schemes for …