Deep adaptive sampling for surrogate modeling without labeled data
From MaRDI portal
Publication:6639518
DOI10.1007/s10915-024-02711-1MaRDI QIDQ6639518
Kejun Tang, Jiayu Zhai, Chao Yang, Xili Wang, Xiaoliang Wan
Publication date: 15 November 2024
Published in: Journal of Scientific Computing (Search for Journal in Brave)
Could not fetch data.
Probabilistic methods, particle methods, etc. for boundary value problems involving PDEs (65N75) Numerical solution of discretized equations for boundary value problems involving PDEs (65N22) Numerical approximation of high-dimensional functions; sparse grids (65D40)
Cites Work
- Title not available (Why is that?)
- Title not available (Why is that?)
- Title not available (Why is that?)
- Reduced basis techniques for stochastic problems
- Automated solution of differential equations by the finite element method. The FEniCS book
- Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification
- The Deep Ritz Method: a deep learning-based numerical algorithm for solving variational problems
- Certified reduced basis methods for parametrized elliptic optimal control problems with distributed controls
- High-Re solutions for incompressible flow using the Navier-Stokes equations and a multigrid method
- DGM: a deep learning algorithm for solving partial differential equations
- Error analysis for physics-informed neural networks (PINNs) approximating Kolmogorov PDEs
- PhyGeoNet: physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain
- PFNN: a penalty-free neural network method for solving a class of second-order boundary-value problems on complex geometries
- Bayesian multiscale deep generative model for the solution of high-dimensional inverse problems
- Adaptive deep density approximation for Fokker-Planck equations
- When and why PINNs fail to train: a neural tangent kernel perspective
- Physics-informed neural networks for high-speed flows
- Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data
- Conservative physics-informed neural networks on discrete domains for conservation laws: applications to forward and inverse problems
- A robust error estimator and a residual-free error indicator for reduced basis methods
- Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data
- An adaptive reduced basis ANOVA method for high-dimensional Bayesian inverse problems
- The role of surrogate models in the development of digital twins of dynamic systems
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Scalable posterior approximations for large-scale Bayesian inverse problems via likelihood-informed parameter and state reduction
- A tutorial on the cross-entropy method
- A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks
- DAS-PINNs: a deep adaptive sampling method for solving high-dimensional partial differential equations
- Active learning based sampling for high-dimensional nonlinear partial differential equations
- Reduced Basis Method for Parametrized Elliptic Optimal Control Problems
- Adaptive Construction of Surrogates for the Bayesian Solution of Inverse Problems
- Inverse problems: A Bayesian perspective
- Nonlinear Model Reduction via Discrete Empirical Interpolation
- The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations
- Solving high-dimensional partial differential equations using deep learning
- Nonlinear methods for model reduction
- Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
- DeepXDE: A Deep Learning Library for Solving Differential Equations
- High-Dimensional Data Analysis with Low-Dimensional Models
- Reduced Basis Collocation Methods for Partial Differential Equations with Random Coefficients
- Reduced Basis Methods for Partial Differential Equations
- Data‐driven physics‐based digital twins via a library of component‐based reduced‐order models
- A digital twin framework for civil engineering structures
- Failure-Informed Adaptive Sampling for PINNs
- AONN: An Adjoint-Oriented Neural Network Method for All-At-Once Solutions of Parametric Optimal Control Problems
- Coupling parameter and particle dynamics for adaptive sampling in Neural Galerkin schemes
- AONN-2: an adjoint-oriented neural network method for PDE-constrained shape optimization
- Failure-informed adaptive sampling for PINNs. II: Combining with re-sampling and subset simulation
This page was built for publication: Deep adaptive sampling for surrogate modeling without labeled data
Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6639518)