Improving Reduced-Order Rotating Detonation Engine Models with Data Assimilation and Machine Learning

arXiv:2609.16237 · physics.flu-dyn, cs.LG, math.DS, physics.comp-ph · Submitted 2026-09-14 · Read on arXiv

physics.flu-dyn, cs.LG, math.DS, physics.comp-ph

Submitted: 2026-09-14

Updated: 2026-09-14

Comments: 20 pages, 12 figures

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Fidelity — 67× in this paper · explained in 2 episodes
• Adam — 4× in this paper · explained in 2 episodes
• DeepONet — 2× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Direct Numerical Simulation — 1× in this paper
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Latent Dynamics — 1× in this paper · explained in 2 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Neural Surrogates — 1× in this paper · explained in 2 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• System Identification — 1× in this paper · explained in 3 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• Solver-in-the-loop training of deep learning closures for large-eddy simulation of turbulent premixed jet flames
• Interpolated Discrepancy Data Assimilation for PDEs with Sparse Observations
• Data Assimilation in Large Eddy Simulation: Addressing Model-Observation Mismatch from Navier-Stokes Data
• Deep Learning of Solver-Aware Turbulence Closures from Nudged LES Dynamics

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