Certified data-driven physics-informed greedy auto-encoder simulator смотреть онлайн

This work is presented at Machine Learning and the Physical Sciences Workshop at the 36th conference on Neural Information Processing Systems (NeurIPS)(https://ml4physicalsciences.github.io/2022).
Paper: https://ml4physicalsciences.github.io/2022/files/NeurIPS_ML4PS_2022_26.pdf
Poster: https://neurips.cc/media/PosterPDFs/NeurIPS%202022/56842.png

Abstract:
A parametric adaptive greedy Latent Space Dynamics Identification (gLaSDI) framework is developed for accurate, efficient, and certified data-driven physics-informed greedy auto-encoder simulators of high-dimensional nonlinear dynamical systems. In the proposed framework, an auto-encoder and dynamics identification models are trained interactively to discover intrinsic and simple latent-space dynamics. To effectively explore the parameter space for optimal model performance, an adaptive greedy sampling algorithm integrated with a physics-informed error indicator is introduced to search for optimal training samples on the fly, outperforming the conventional predefined uniform sampling. Further, an efficient k-nearest neighbor convex interpolation scheme is employed to exploit local latent-space dynamics for improved predictability. Numerical results demonstrate that the proposed method achieves 121 to 2,658x speed-up with 1 to 5% relative errors for radial advection dynamical problems.

The code can be found at https://github.com/LLNL/libROM/tree/gLaSDI/python/gLaSDI

For more details, please refer to the wonderful talk by Dr. Youngsoo Choi (https://www.youtube.com/watch?v=DS-PM2lYkUk&ab_channel=soopsori) and our papers:

[1] He, X., Choi, Y., Fries, W. D., Belof, J., & Chen, J. S. (2022). gLaSDI: Parametric Physics-informed Greedy Latent Space Dynamics Identification. arXiv preprint arXiv:2204.12005.

[2] Fries, W. D., He, X., & Choi, Y. (2022). Lasdi: Parametric latent space dynamics identification. Computer Methods in Applied Mechanics and Engineering, 399, 115436.