Retrieval of Canopy Closure and LAI of Moso Bamboo Forest Using Spectral Mixture Analysis Based on Real Scenario Simulation. - Huaqiang Du; Weiliang Fan; Guomo Zhou; Xiaojun Xu; Hongli Ge; Yongjun Shi; Yufeng Zhou; Ruirui Cui; Yulong Lu (IEEE Trans. Geosci. Remote. Sens., 2011)

Gaussian Approximations for Probability Measures on Rd. - Yulong Lu; Andrew M. Stuart; Hendrik Weber (SIAM/ASA J. Uncertain. Quantification, 2017)

Gaussian Approximations for Transition Paths in Brownian Dynamics. - Yulong Lu; Andrew M. Stuart; Hendrik Weber (SIAM J. Math. Anal., 2017)

Uniform-in-Time Weak Error Analysis for Stochastic Gradient Descent Algorithms via Diffusion Approximation. - Yuanyuan Feng; Tingran Gao; Lei Li 006; Jian-Guo Liu 006; Yulong Lu (CoRR, 2019)

Accelerating Langevin Sampling with Birth-death. - Yulong Lu; Jianfeng Lu 001; James Nolen (CoRR, 2019)

Scaling Limit of the Stein Variational Gradient Descent: The Mean Field Regime. - Jianfeng Lu 001; Yulong Lu; James Nolen (SIAM J. Math. Anal., 2019)

A Mean Field Analysis Of Deep ResNet And Beyond: Towards Provably Optimization Via Overparameterization From Depth. - Yiping Lu 001; Chao Ma 012; Yulong Lu; Jianfeng Lu 001; Lexing Ying (ICML, 2020)

A Universal Approximation Theorem of Deep Neural Networks for Expressing Probability Distributions. - Yulong Lu; Jianfeng Lu 001 (NeurIPS, 2020)

A Mean-field Analysis of Deep ResNet and Beyond: Towards Provable Optimization Via Overparameterization From Depth. - Yiping Lu 001; Chao Ma 012; Yulong Lu; Jianfeng Lu 001; Lexing Ying (CoRR, 2020)

A Universal Approximation Theorem of Deep Neural Networks for Expressing Distributions. - Yulong Lu; Jianfeng Lu 001 (CoRR, 2020)

Continuum limit and preconditioned Langevin sampling of the path integral molecular dynamics. - Jianfeng Lu 001; Yulong Lu; Zhennan Zhou (J. Comput. Phys., 2020)

Quantitative Propagation of Chaos in a Bimolecular Chemical Reaction-Diffusion Model. - Tau Shean Lim; Yulong Lu; James Nolen (SIAM J. Math. Anal., 2020)

A Priori Generalization Analysis of the Deep Ritz Method for Solving High Dimensional Elliptic Partial Differential Equations. - Yulong Lu; Jianfeng Lu 001; Min Wang (COLT, 2021)

On the Representation of Solutions to Elliptic PDEs in Barron Spaces. - Ziang Chen; Jianfeng Lu 001; Yulong Lu (NeurIPS, 2021)

A Priori Generalization Analysis of the Deep Ritz Method for Solving High Dimensional Elliptic Equations. - Jianfeng Lu 001; Yulong Lu; Min Wang (CoRR, 2021)

A Priori Generalization Error Analysis of Two-Layer Neural Networks for Solving High Dimensional Schrödinger Eigenvalue Problems. - Jianfeng Lu 001; Yulong Lu (CoRR, 2021)

Solving multiscale steady radiative transfer equation using neural networks with uniform stability. - Yulong Lu; Li Wang; Wuzhe Xu (CoRR, 2021)

The Establishment of Ore-Controlling Fracture System of Baoginshan Gold Mine Based on Fracture-Tectonic Analysis. - Yulong Lu; Xingqiang Li; Yang Liu 281; Jiahao Leng (Mob. Inf. Syst., 2021)

A Regularity Theory for Static Schrödinger Equations on Rd in Spectral Barron Spaces. - Ziang Chen; Jianfeng Lu 001; Yulong Lu; Shengxuan Zhou (CoRR, 2022)

Exponential-Wrapped Distributions on Symmetric Spaces. - Emmanuel Chevallier; Didong Li; Yulong Lu; David B. Dunson (SIAM J. Math. Data Sci., 2022)

Transfer Learning Enhanced DeepONet for Long-Time Prediction of Evolution Equations. - Wuzhe Xu; Yulong Lu; Li Wang (AAAI, 2023)

Two-Scale Gradient Descent Ascent Dynamics Finds Mixed Nash Equilibria of Continuous Games: A Mean-Field Perspective. - Yulong Lu (ICML, 2023)

Optimal Deep Neural Network Approximation for Korobov Functions with respect to Sobolev Norms. - Yahong Yang; Yulong Lu (CoRR, 2023)

Score-based generative models break the curse of dimensionality in learning a family of sub-Gaussian distributions. - Frank Cole; Yulong Lu (ICLR, 2024)

Score-based generative models break the curse of dimensionality in learning a family of sub-Gaussian probability distributions. - Frank Cole; Yulong Lu (CoRR, 2024)

Fully discretized Sobolev gradient flow for the Gross-Pitaevskii eigenvalue problem. - Ziang Chen; Jianfeng Lu 001; Yulong Lu; Xiangxiong Zhang (CoRR, 2024)

Provable In-Context Learning of Linear Systems and Linear Elliptic PDEs with Transformers. - Frank Cole; Yulong Lu; Riley O'Neill; Tianhao Zhang (CoRR, 2024)

Near-optimal deep neural network approximation for Korobov functions with respect to Lp and H1 norms. - Yahong Yang; Yulong Lu (Neural Networks, 2024)

On the Convergence of Sobolev Gradient Flow for the Gross-Pitaevskii Eigenvalue Problem. - Ziang Chen; Jianfeng Lu 001; Yulong Lu; Xiangxiong Zhang (SIAM J. Numer. Anal., 2024)

Generative Downscaling of PDE Solvers with Physics-Guided Diffusion Models. - Yulong Lu; Wuzhe Xu (J. Sci. Comput., 2024)

In-Context Learning of Linear Dynamical Systems with Transformers: Error Bounds and Depth-Separation. - Frank Cole; Yulong Lu; Tianhao Zhang; Yuxuan Zhao (CoRR, 2025)

Diffusion-based Models for Unpaired Super-resolution in Fluid Dynamics. - Wuzhe Xu; Yulong Lu; Lian Shen; Anqing Xuan; Ali Barzegari (CoRR, 2025)

Convergence of Time-Averaged Mean Field Gradient Descent Dynamics for Continuous Multi-Player Zero-Sum Games. - Yulong Lu; Pierre Monmarché (CoRR, 2025)

Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution. - Wuzhe Xu; Yulong Lu; Sifan Wang; Tong-Rui Liu (CoRR, 2025)