Search Results for author:
Found 8 papers, 2 papers with code
Deep adaptive sampling for surrogate modeling without labeled data
In this work, we present a deep adaptive sampling method for surrogate modeling ($\text{DAS}^2$), where we generalize the deep adaptive sampling (DAS) method [62] [Tang, Wan and Yang, 2023] to build surrogate models for low-regularity parametric differential equations.
Adversarial Adaptive Sampling: Unify PINN and Optimal Transport for the Approximation of PDEs
The key idea is to use a deep generative model to adjust random samples in the training set such that the residual induced by the approximate PDE solution can maintain a smooth profile when it is being minimized.
Dimension-reduced KRnet maps for high-dimensional Bayesian inverse problems
We present a dimension-reduced KRnet map approach (DR-KRnet) for high-dimensional Bayesian inverse problems, which is based on an explicit construction of a map that pushes forward the prior measure to the posterior measure in the latent space.
DAS-PINNs: A deep adaptive sampling method for solving high-dimensional partial differential equations
In this work we propose a deep adaptive sampling (DAS) method for solving partial differential equations (PDEs), where deep neural networks are utilized to approximate the solutions of PDEs and deep generative models are employed to generate new collocation points that refine the training set.
Augmented KRnet for density estimation and approximation
In the augmented KRnet, a fully nonlinear update is achieved in two iterations.
Adaptive deep density approximation for Fokker-Planck equations
In this paper we present an adaptive deep density approximation strategy based on KRnet (ADDA-KR) for solving the steady-state Fokker-Planck (F-P) equations.
Tensor Train Random Projection
This work proposes a novel tensor train random projection (TTRP) method for dimension reduction, where pairwise distances can be approximately preserved.
D3M: A deep domain decomposition method for partial differential equations
A state-of-the-art deep domain decomposition method (D3M) based on the variational principle is proposed for partial differential equations (PDEs).
