A sample-efficient deep learning method for multivariate uncertainty qualification of acoustic-vibration interaction problems
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Publication:2138808
DOI10.1016/j.cma.2022.114784OpenAlexW4220764579MaRDI QIDQ2138808
Ruhui Cheng, Shengze Li, H. Lian, Chang-Jun Zheng, Lei-Lei Chen, Stéphane Pierre Alain Bordas
Publication date: 12 May 2022
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.cma.2022.114784
Monte Carlo simulationisogeometric analysisdeep neural networkFEM/BEM couplingvibro-acoustic analysisSVD-RBF
Vibrations in dynamical problems in solid mechanics (74H45) Stochastic and other probabilistic methods applied to problems in solid mechanics (74S60)
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Uses Software
Cites Work
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- Multi-output local Gaussian process regression: applications to uncertainty quantification
- A wideband fast multipole boundary element method for three dimensional acoustic shape sensitivity analysis based on direct differentiation method
- Proper orthogonal decomposition and Monte Carlo based isogeometric stochastic method for material, geometric and force multi-dimensional uncertainties
- Constrained probabilistic collocation method for uncertainty quantification of geophysical models
- Gaussian processes with built-in dimensionality reduction: applications to high-dimensional uncertainty propagation
- Uncertainty propagation using infinite mixture of gaussian processes and variational Bayesian inference
- Isogeometric analysis: CAD, finite elements, NURBS, exact geometry and mesh refinement
- Developments in structural-acoustic optimization for passive noise control
- Is the Burton-Miller formulation really free of fictitious eigenfrequencies?
- Acceleration of isogeometric boundary element analysis through a black-box fast multipole method
- Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification
- Modeling uncertainty in steady state diffusion problems via generalized polynomial chaos
- Modeling uncertainty in flow simulations via generalized polynomial chaos.
- An isogeometric approach of two dimensional acoustic design sensitivity analysis and topology optimization analysis for absorbing material distribution
- Structural shape optimization of three dimensional acoustic problems with isogeometric boundary element methods
- Deep UQ: learning deep neural network surrogate models for high dimensional uncertainty quantification
- Deep learned finite elements
- Uncertainty quantification of spatially uncorrelated loads with a reduced-order stochastic isogeometric method
- Multilevel Monte Carlo method for topology optimization of flexoelectric composites with uncertain material properties
- Bi-material topology optimization for fully coupled structural-acoustic systems with isogeometric FEM-BEM
- Model order reduction accelerated Monte Carlo stochastic isogeometric method for the analysis of structures with high-dimensional and independent material uncertainties
- An adaptive SVD-Krylov reduced order model for surrogate based structural shape optimization through isogeometric boundary element method
- Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data
- Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data
- Computational mechanics enhanced by deep learning
- An energy approach to the solution of partial differential equations in computational mechanics via machine learning: concepts, implementation and applications
- Acoustic topology optimization of sound absorbing materials directly from subdivision surfaces with isogeometric boundary element methods
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- An application of fast multipole method to isogeometric boundary element method for Laplace equation in two dimensions
- A Stochastic Radial Basis Function Method for the Global Optimization of Expensive Functions
- The Burton and Miller Method: Unlocking Another Mystery of Its Coupling Parameter
- MONTE CARLO METHODS FOR SOLVING MULTIVARIABLE PROBLEMS
- Stochastic perturbation finite elements
- The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations
- The Stochastic Perturbation Method for Computational Mechanics
- Multidimensional Adaptive Relevance Vector Machines for Uncertainty Quantification
- 不确定性量化的高精度数值方法和理论
- The application of integral equation methods to the numerical solution of some exterior boundary-value problems
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