mlsanityData to 7 Oct 2026

Generalized Multiscale Finite Element Methods (GMsFEM)

arXiv 1301.2866 ↗First posted 14 Jan 2013math.NA cs.CE cs.NAcited by 21

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Abstract (arXiv)

In this paper, we propose a general approach called Generalized Multiscale Finite Element Method (GMsFEM) for performing multiscale simulations for problems without scale separation over a complex input space. As in multiscale finite element methods (MsFEMs), the main idea of the proposed approach is to construct a small dimensional local solution space that can be used to generate an efficient and accurate approximation to the multiscale solution with a potentially high dimensional input parameter space. In the proposed approach, we present a general procedure to construct the offline space that is used for a systematic enrichment of the coarse solution space in the online stage. The enrichment in the online stage is performed based on a spectral decomposition of the offline space. In the online stage, for any input parameter, a multiscale space is constructed to solve the global problem on a coarse grid. The online space is constructed via a spectral decomposition of the offline space and by choosing the eigenvectors corresponding to the largest eigenvalues. The computational saving is due to the fact that the construction of the online multiscale space for any input parameter is fast and this space can be re-used for solving the forward problem with any forcing and boundary condition. Compared with the other approaches where global snapshots are used, the local approach that we present in this paper allows us to eliminate unnecessary degrees of freedom on a coarse-grid level. We present various examples in the paper and some numerical results to demonstrate the effectiveness of our method.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Jan 2013.

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Citing papers per year: 2021: 5; 2022: 5; 2023: 1; 2024: 5; 2025: 2; 2026: 3.

21 papers.

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  1. 2022Interfacing Finite Elements with Deep Neural Operators for Fast Multiscale Modeling of Mechanics Problemscited by 47
  2. 2022Mitigating spectral bias for the multiscale operator learningcited by 43
  3. 2021A data-driven peridynamic continuum model for upscaling molecular dynamicscited by 17
  4. 2025Defining Foundation Models for Computational Science: A Call for Clarity and Rigorcited by 16
  5. 2021Subspace Decomposition based DNN algorithm for elliptic type multi-scale PDEscited by 10
  6. 2021A Gradient-based Deep Neural Network Model for Simulating Multiphase Flow in Porous Mediacited by 8
  7. 2021A Physics-Constrained Deep Learning Model for Simulating Multiphase Flow in 3D Heterogeneous Porous Mediacited by 6
  8. 2022A Neural Network Approach for Homogenization of Multiscale Problemscited by 5
  9. 2022Bayesian Nonlocal Operator Regression (BNOR): A Data-Driven Learning Framework of Nonlocal Models with Uncertainty Quantificationcited by 3
  10. 2023Towards a unified nonlocal, peridynamics framework for the coarse-graining of molecular dynamics data with fracturescited by 3
  11. 2021A Forward Backward Greedy approach for Sparse Multiscale Learningcited by 2
  12. 2022AMS-Net: Adaptive Multiscale Sparse Neural Network with Interpretable Basis Expansion for Multiphase Flow Problemscited by 2
9 more
  1. 2024Prediction of Multiscale Features Using Deep Learning-based Preconditioner-Solver Architecture for Darcy Equation in High-Contrast Mediacited by 1
  2. 2024Multiscale Corrections by Continuous Super-Resolutioncited by 1
  3. 2024Stochastic approach for elliptic problems in perforated domainscited by 0
  4. 2024Learning-based Multi-continuum Model for Multiscale Flow Problemscited by 0
  5. 2024Embedded Nonlocal Operator Regression (ENOR): Quantifying model error in learning nonlocal operatorscited by 0
  6. 2025Dual-Domain Deep Learning Method to Accelerate Local Basis Functions Computation for Reservoir Simulation in High-Contrast Porous Mediacited by 0
  7. 2026Applying Two-Grid Preconditioner for Subsurface Flow Simulation using Attention-enhanced Hybrid Network to Accelerate Multiscale Discretization in High-contrast Mediacited by 0
  8. 2026The Frame Kernel Method for Multiscale Operator Learningcited by 0
  9. 2026Gaussian-process surrogate indicators for residual-based adaptive GMsFEMcited by 0