Advancing wave equation analysis in dual-continuum systems: a partial learning approach with discrete empirical interpolation and deep neural networks
DOI10.1016/J.CAM.2024.115755MaRDI QIDQ6489267
Dmitry Ammosov, Uygulaana Kalachikova
Publication date: 19 April 2024
Published in: Journal of Computational and Applied Mathematics (Search for Journal in Brave)
proper orthogonal decompositionwave equationsdiscrete empirical interpolation methoddeep neural networkexplicit-implicit schememulticontinuum homogenization
Basic methods in fluid mechanics (76Mxx) Numerical methods for partial differential equations, initial value and time-dependent initial-boundary value problems (65Mxx) Numerical methods for partial differential equations, boundary value problems (65Nxx)
Cites Work
- Unnamed Item
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- Stochastic collocation and mixed finite elements for flow in porous media
- Constraint energy minimizing generalized multiscale finite element method
- Non-local multi-continua upscaling for flows in heterogeneous fractured media
- Preconditioning Markov chain Monte Carlo method for geomechanical subsidence using multiscale method and machine learning technique
- Multicontinuum homogenization and its relation to nonlocal multicontinuum theories
- B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data
- Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems
- Contrast-independent partially explicit time discretizations for multiscale wave problems
- Efficient hybrid explicit-implicit learning for multiscale problems
- Machine learning for accelerating macroscopic parameters prediction for poroelasticity problem in stochastic media
- POD-DEIM model order reduction for strain-softening viscoplasticity
- Wavelet neural operator for solving parametric partial differential equations in computational mechanics problems
- Hybrid explicit-implicit learning for multiscale problems with time dependent source
- Nonlinear Model Reduction via Discrete Empirical Interpolation
- Mixed Multiscale Finite Element Methods for Stochastic Porous Media Flows
- Multiscale Finite Element Methods
- A New Look at Proper Orthogonal Decomposition
- Reduced Basis Methods for Partial Differential Equations
- On numerical homogenization of shale gas transport
- Prediction of numerical homogenization using deep learning for the Richards equation
- Partial learning using partially explicit discretization for multicontinuum/multiscale problems. Fractured poroelastic media simulation
- Partial learning using partially explicit discretization for multicontinuum/multiscale problems with limited observation: language interactions simulation
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