Numerical Analysis on Neural Network Projected Schemes for Approximating One Dimensional Wasserstein Gradient Flows
<|reference_start|>Numerical Analysis on Neural Network Projected Schemes for Approximating One Dimensional Wasserstein Gradient Flows: We provide a numerical analysis and computation of neural network projected schemes for approximating one dimensional Wasserstein gradient flows. We approximate the Lagrangian mapping functions of gradient flows by the class of two-layer neural network functions with ReLU (rectified linear unit) activation functions. The numerical scheme is based on a projected gradient method, namely the Wasserstein natural gradient, where the projection is constructed from the $L^2$ mapping spaces onto the neural network parameterized mapping space. We establish theoretical guarantees for the performance of the neural projected dynamics. We derive a closed-form update for the scheme with well-posedness and explicit consistency guarantee for a particular choice of network structure. General truncation error analysis is also established on the basis of the projective nature of the dynamics. Numerical examples, including gradient drift Fokker-Planck equations, porous medium equations, and Keller-Segel models, verify the accuracy and effectiveness of the proposed neural projected algorithm.<|reference_end|>
@article{zuo2024numerical,
    title={Numerical Analysis on Neural Network Projected Schemes for Approximating
  One Dimensional Wasserstein Gradient Flows},
    author={Xinzhe Zuo, Jiaxi Zhao, Shu Liu, Stanley Osher, Wuchen Li},
    journal={arXiv preprint arXiv:2402.16821},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.16821},
    primaryClass={math.NA cs.NA math.OC}
}