Search Results for author: Peyman Givi

Found 3 papers, 0 papers with code

Physics-enhanced Neural Operator for Simulating Turbulent Transport

no code implementations31 May 2024 Shengyu Chen, Peyman Givi, Can Zheng, Xiaowei Jia

The precise simulation of turbulent flows is of immense importance in a variety of scientific and engineering fields, including climate science, freshwater science, and the development of energy-efficient manufacturing processes.

Reconstructing Turbulent Flows Using Physics-Aware Spatio-Temporal Dynamics and Test-Time Refinement

no code implementations24 Apr 2023 Shengyu Chen, Tianshu Bao, Peyman Givi, Can Zheng, Xiaowei Jia

The results on two different types of turbulent flow data confirm the superiority of the proposed method in reconstructing the high-resolution DNS data and preserving the physical characteristics of flow transport.

Super-Resolution

Reconstructing High-resolution Turbulent Flows Using Physics-Guided Neural Networks

no code implementations6 Sep 2021 Shengyu Chen, Shervin Sammak, Peyman Givi, Joseph P. Yurko1, Xiaowei Jia

Direct numerical simulation (DNS) of turbulent flows is computationally expensive and cannot be applied to flows with large Reynolds numbers.

Super-Resolution Vocal Bursts Intensity Prediction

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