Real-Time Reconstruction of Fluid Flow under Unknown Disturbance

ACM Transactions on Graphics (SIGGRAPH Asia 2023)

Kinfung Chu¹ Jiawei Huang² Hidemasa Takana¹ Yoshifumi Kitamura¹

¹Tohoku University ²Chuzhou University and Void Dimensions

Our reconstruction method recreates water flow based on only a small amount of data from real water. Through reinforcement learning, we recover the detailed water motion from the movement of a few buoys.

Results

Move cursor over the video to adjust the split position.
Left / Right: Truth / Reconstruction

BibTeX

        
@article{chu2024flowrecon,
    author = {Chu, Kinfung and Huang, Jiawei and Takana, Hidemasa and Kitamura, Yoshifumi},
    title = {Real-Time Reconstruction of Fluid Flow under Unknown Disturbance},
    year = {2023},
    issue_date = {February 2024},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    volume = {43},
    number = {1},
    issn = {0730-0301},
    url = {https://doi.org/10.1145/3624011},
    doi = {10.1145/3624011},
    journal = {ACM Trans. Graph.},
    month = oct,
    articleno = {4},
    numpages = {14},
    keywords = {parameter optimization, SPH, Fluid simulation, reinforcement learning, fluid tracking}
}