• Corpus ID: 238531406

A composable autoencoder-based iterative algorithm for accelerating numerical simulations

@article{Ranade2021ACA,
  title={A composable autoencoder-based iterative algorithm for accelerating numerical simulations},
  author={Rishikesh Ranade and Chris Hill and Haiyang He and Amir Maleki and Norman Chang and Jay Pathak},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.03780}
}
Numerical simulations for engineering applications solve partial differential equations (PDE) to model various physical processes. Traditional PDE solvers are very accurate but computationally costly. On the other hand, Machine Learning (ML) methods offer a significant computational speedup but face challenges with accuracy and generalization to different PDE conditions, such as geometry, boundary conditions, initial conditions and PDE source terms. In this work, we propose a novel ML-based… 
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