1 citations · 2 across the 2 of their papers we have counts for
2 papers
physics.flu-dyn2025★ 1 cited
Resolving Turbulent Magnetohydrodynamics: A Hybrid Operator-Diffusion Framework
Semih Kacmaz, E. A. Huerta, Roland Haas
We present a hybrid machine learning framework that combines Physics-Informed Neural Operators (PINOs) with score-based generative diffusion models to simulate the full spatio-temp…
gr-qc2024★ 1 cited
Machine learning-driven conservative-to-primitive conversion in hybrid piecewise polytropic and tabulated equations of state
Semih Kacmaz, Roland Haas, E. A. Huerta
We present a novel machine learning (ML) method to accelerate conservative-to-primitive inversion, focusing on hybrid piecewise polytropic and tabulated equations of state. Traditi…