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cs.LGApr 6, 2021
1
citations (OpenAlex)
authors
  • Rishikesh Ranade
  • Chris Hill
  • Haiyang He
  • Amir Maleki
  • Jay Pathak
arXiv abstractPDF
paper

A Latent space solver for PDE generalization

arXiv:2104.02452

Abstract

In this work we propose a hybrid solver to solve partial differential equation (PDE)s in the latent space. The solver uses an iterative inferencing strategy combined with solution initialization to improve generalization of PDE solutions. The solver is tested on an engineering case and the results show that it can generalize well to several PDE conditions.

References in corpus (10)

  • Hidden Physics Models: Machine Learning of Nonlinear Partial Differential Equations
  • Learning data driven discretizations for partial differential equations
  • Nonlinear mode decomposition with convolutional neural networks for fluid dynamics
  • Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoencoders
  • Convolutional neural network based hierarchical autoencoder for nonlinear mode decomposition of fluid field data
  • Learned discretizations for passive scalar advection in a 2-D turbulent flow
  • Learning to Control PDEs with Differentiable Physics
  • Differentiable Molecular Simulations for Control and Learning
  • Turbulence forecasting via Neural ODE
  • An unsupervised learning approach to solving heat equations on chip based on Auto Encoder and Image Gradient

Cited by in corpus (1)

  • A composable autoencoder-based iterative algorithm for accelerating numerical simulations
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