pith. sign in

arxiv: 2209.12572 · v4 · pith:N22SG6S3new · submitted 2022-09-26 · ⚛️ physics.comp-ph · physics.chem-ph

Solving Schr\"{o}dinger Equation Using Tensor Neural Network

classification ⚛️ physics.comp-ph physics.chem-ph
keywords tensorequationnetworkneuralnumericalaccuracymany-bodyschrodinger
0
0 comments X
read the original abstract

In this paper, we introduce a novel approach to solve the many-body Schrodinger equation by the tensor neural network. Based on the tensor product structure, we can do the direct numerical integration by using fixed quadrature points for the functions constructed by the tensor neural network within tolerable computational complexity. Especially, we design several types of efficient numerical methods to treat the variable-coupled Coulomb potentials with high accuracy. The corresponding machine learning method is built for solving many-body Schrodinger equation. Some numerical examples are provided to validate the accuracy and efficiency of the proposed algorithms.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Parameterized Representations via Implicit Stochastic Modulation for High-Dimensional and High-Order Neural PDE Solvers

    cs.LG 2026-06 unverdicted novelty 7.0

    PRISM enables zero-shot parameterized high-dimensional high-order neural PDE solvers via implicit stochastic modulation that decouples parameters from the differentiation graph while preserving unbiased estimators.

  2. fTNN: a tensor neural network for fractional PDEs

    cs.LG 2026-06 unverdicted novelty 6.0

    fTNN is a deterministic tensor neural network subspace method for fractional PDEs that decomposes the fractional Laplacian via spatially dependent integration splits and uses boundary-singularity-aware trial functions...

  3. Regularity Analysis and Tensor Neural Network Methods for Quasiperiodic Elliptic Equations

    math.NA 2026-04 unverdicted novelty 6.0

    Tensor neural networks with projection solve quasiperiodic elliptic equations after proving regularity under Diophantine conditions and a source-term restriction.

  4. Stochastic-Dimension Frozen Sampled Neural Network for High-Dimensional Gross-Pitaevskii Equations on Unbounded Domains

    cs.LG 2026-04 unverdicted novelty 6.0

    SD-FSNN combines stochastic dimension sampling, frozen random weights, Gaussian ansatz, and structure-preserving layers to solve high-dimensional GPEs on unbounded domains with dimension-independent cost and improved ...

  5. Lower Bound on the Representation Complexity of Antisymmetric Tensor Product Functions

    math.NA 2025-01 unverdicted novelty 6.0

    Minimum number of terms for exact antisymmetry in a class of TPFs grows exponentially with dimension, shown via CP rank of antisymmetric tensors.