mlsanityData to 7 Oct 2026

Fast Neural Network based Solving of Partial Differential Equations

arXiv 2205.08978 ↗First posted 18 May 2022cs.LG cs.NA math.NAcited by 1

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Abstract (arXiv)

We present a novel method for using Neural Networks (NNs) for finding solutions to a class of Partial Differential Equations (PDEs). Our method builds on recent advances in Neural Radiance Field research (NeRFs) and allows for a NN to converge to a PDE solution much faster than classic Physically Informed Neural Network (PINNs) approaches.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 4 of its 10 references that are in the map. First posted on arXiv 18 May 2022.

Builds on

4 of 10 references.

References · 4

  1. 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,698
  2. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,214
  3. 2017DGM: A deep learning algorithm for solving partial differential equationscited by 674
  4. 2020NVIDIA SimNet^{TM}: an AI-accelerated multi-physics simulation frameworkcited by 58

Built on by

Cited by year

Citing papers per year: 2023: 1.

1 paper.

  1. 2023LatentPINNs: Generative physics-informed neural networks via a latent representation learningcited by 5