mlsanityData to 14 Oct 2026

Elvet -- a neural network-based differential equation and variational problem solver

arXiv 2103.14575 ↗First posted 26 Mar 2021cs.LG hep-lat hep-phcited by 7

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Abstract (arXiv)

We present Elvet, a Python package for solving differential equations and variational problems using machine learning methods. Elvet can deal with any system of coupled ordinary or partial differential equations with arbitrary initial and boundary conditions. It can also minimize any functional that depends on a collection of functions of several variables while imposing constraints on them. The solution to any of these problems is represented as a neural network trained to produce the desired function.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 10 of its 39 references that are in the map. First posted on arXiv 26 Mar 2021.

Builds on

10 of 39 references.

References · 10

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2020Fourier Neural Operator for Parametric Partial Differential Equationscited by 2,908
  3. 2017DGM: A deep learning algorithm for solving partial differential equationscited by 678
  4. 2017Solving high-dimensional partial differential equations using deep learningcited by 556
  5. 2017Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equationscited by 373
  6. 2017The Expressive Power of Neural Networks: A View from the Widthcited by 320
  7. 2017Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equationscited by 161
  8. 2018Neural Networks Trained to Solve Differential Equations Learn General Representationscited by 16
  9. 2019A Discussion on Solving Partial Differential Equations using Neural Networkscited by 13
  10. 2021dNNsolve: an efficient NN-based PDE solvercited by 4

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Cited by year

Citing papers per year: 2021: 2; 2022: 2; 2023: 0; 2024: 2; 2025: 1.

7 papers.