MuFiNNs integrates sparse experimental measurements with structured low-fidelity models via hierarchical construction and nonlinear correction to predict 3D flame wrinkling dynamics and turbulent mass burning velocity across fuels, pressures, and turbulence levels.
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A non-intrusive framework combines Koopman autoencoders with a spatio-temporal surrogate to learn and predict physics-constrained dynamics of systems like 2D flow around a cylinder for unseen conditions.
citing papers explorer
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Hierarchical Multi-Fidelity Learning for Predicting Three-Dimensional Flame Wrinkling and Turbulent Burning Velocity
MuFiNNs integrates sparse experimental measurements with structured low-fidelity models via hierarchical construction and nonlinear correction to predict 3D flame wrinkling dynamics and turbulent mass burning velocity across fuels, pressures, and turbulence levels.
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Non-intrusive Learning of Physics-Informed Spatio-temporal Surrogate for Accelerating Design
A non-intrusive framework combines Koopman autoencoders with a spatio-temporal surrogate to learn and predict physics-constrained dynamics of systems like 2D flow around a cylinder for unseen conditions.