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09/13/2023
An Extreme Learning Machine-Based Method for Computational PDEs in Higher Dimensions
We present two effective methods for solving high-dimensional partial di...
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08/24/2023
Block-Level Interference Exploitation Precoding for MU-MISO: An ADMM Approach
We study constructive interference based block-level precoding (CI-BLP) ...
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08/06/2023
A clever neural network in solving inverse problems of Schrödinger equation
In this work, we solve inverse problems of nonlinear Schrödinger equatio...
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08/04/2023
Diffusion-Augmented Depth Prediction with Sparse Annotations
Depth estimation aims to predict dense depth maps. In autonomous driving...
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07/17/2023
Neural Video Depth Stabilizer
Video depth estimation aims to infer temporally consistent depth. Some m...
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06/15/2023
Low-Switching Policy Gradient with Exploration via Online Sensitivity Sampling
Policy optimization methods are powerful algorithms in Reinforcement Lea...
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06/14/2023
Gauss Newton method for solving variational problems of PDEs with neural network discretizaitons
The numerical solution of differential equations using machine learning-...
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12/12/2022
Physics-preserving IMPES based multiscale methods for immiscible two-phase flow in highly heterogeneous porous media
In this paper, we propose a physics-preserving multiscale method to solv...
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10/09/2022
A Method for Computing Inverse Parametric PDE Problems with Random-Weight Neural Networks
We present a method for computing the inverse parameters and the solutio...
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09/29/2022
SymmNeRF: Learning to Explore Symmetry Prior for Single-View View Synthesis
We study the problem of novel view synthesis of objects from a single im...
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07/31/2022
Less is More: Consistent Video Depth Estimation with Masked Frames Modeling
Temporal consistency is the key challenge of video depth estimation. Pre...
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03/22/2022
A conservative multiscale method for stochastic highly heterogeneous flow
In this paper, we propose a local model reduction approach for subsurfac...
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11/26/2021
A deep learning based reduced order modeling for stochastic underground flow problems
In this paper, we propose a deep learning based reduced order modeling m...
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07/19/2021
Inverse Problem of Nonlinear Schrödinger Equation as Learning of Convolutional Neural Network
In this work, we use an explainable convolutional neural network (NLS-Ne...
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05/12/2021
A local-global generalized multiscale finite element method for highly heterogeneous stochastic groundwater flow problems
In this paper, we propose a local-global multiscale method for highly he...
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09/30/2020
Constraint energy minimization generalized multiscale finite element method in mixed formulation for parabolic equations
In this paper, we develop the constraint energy minimization generalized...
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08/20/2020
A comparison of mixed multiscale finite element methods for multiphase transport in highly heterogeneous media
In this paper, we systemically review and compare two mixed multiscale f...
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08/03/2020
Two-sample Testing on Latent Distance Graphs With Unknown Link Functions
We propose a valid and consistent test for the hypothesis that two laten...
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06/23/2020
Adaptive multiscale model reduction for nonlinear parabolic equations using GMsFEM
In this paper, we propose a coupled Discrete Empirical Interpolation Met...
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06/23/2020
Online conservative generalized multiscale finite element method for flow models
In this paper, we consider an online enrichment procedure using the Gene...
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06/23/2020
Online conservative generalized multiscale finite element methodto flow models
In this paper, we consider an online enrichment procedure using the Gene...
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11/09/2018
Convolutional neural networks in phase space and inverse problems
We study inverse problems consisting on determining medium properties us...
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03/13/2018