mlsanityData to 7 Oct 2026

Solving High-Dimensional Partial Integral Differential Equations: The Finite Expression Method

arXiv 2410.00835 ↗First posted 1 Oct 2024math.NA cs.LG cs.NAcited by 3

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Abstract (arXiv)

In this paper, we introduce a new finite expression method (FEX) to solve high-dimensional partial integro-differential equations (PIDEs). This approach builds upon the original FEX and its inherent advantages with new advances: 1) A novel method of parameter grouping is proposed to reduce the number of coefficients in high-dimensional function approximation; 2) A Taylor series approximation method is implemented to significantly improve the computational efficiency and accuracy of the evaluation of the integral terms of PIDEs. The new FEX based method, denoted FEX-PG to indicate the addition of the parameter grouping (PG) step to the algorithm, provides both high accuracy and interpretable numerical solutions, with the outcome being an explicit equation that facilitates intuitive understanding of the underlying solution structures. These features are often absent in traditional methods, such as finite element methods (FEM) and finite difference methods, as well as in deep learning-based approaches. To benchmark our method against recent advances, we apply the new FEX-PG to solve benchmark PIDEs in the literature. In high-dimensional settings, FEX-PG exhibits strong and robust performance, achieving relative errors on the order of single precision machine epsilon.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 19 of its 42 references that are in the map. First posted on arXiv 1 Oct 2024.

Builds on

19 of 42 references.

References · 19

  1. 2017DGM: A deep learning algorithm for solving partial differential equationscited by 674
  2. 2022Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's nextcited by 603
  3. 2017The Deep Ritz method: A deep learning-based numerical algorithm for solving variational problemscited by 581
  4. 2017Solving high-dimensional partial differential equations using deep learningcited by 549
  5. 2019Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradientscited by 294
  6. 2020Implicit Bias of Gradient Descent for Wide Two-layer Neural Networks Trained with the Logistic Losscited by 267
  7. 2017Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equationscited by 188
  8. 2020Neural Network Approximation: Three Hidden Layers Are Enoughcited by 63
  9. 2018A proof that artificial neural networks overcome the curse of dimensionality in the numerical approximation of Black-Scholes partial differential equationscited by 55
  10. 2021Deep Network Approximation: Achieving Arbitrary Accuracy with Fixed Number of Neuronscited by 41
  11. 2021Machine Learning For Elliptic PDEs: Fast Rate Generalization Bound, Neural Scaling Law and Minimax Optimalitycited by 40
  12. 2020Solving high-dimensional eigenvalue problems using deep neural networks: A diffusion Monte Carlo like approachcited by 35
7 more
  1. 2022Finite Expression Method for Solving High-Dimensional Partial Differential Equationscited by 21
  2. 2021Solving PDEs on Unknown Manifolds with Machine Learningcited by 12
  3. 2023Finite Expression Methods for Discovering Physical Laws from Datacited by 9
  4. 2023A Finite Expression Method for Solving High-Dimensional Committor Problemscited by 9
  5. 2020An unsupervised deep learning approach in solving partial integro-differential equationscited by 5
  6. 2023Temporal Difference Learning for High-Dimensional PIDEs with Jumpscited by 5
  7. 2021A semigroup method for high dimensional elliptic PDEs and eigenvalue problems based on neural networkscited by 4

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Citing papers per year: 2025: 2; 2026: 1.

3 papers.