Yulong Lu

Assistant Professor

University of Massachusetts Amherst

United States

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About

Yulong Lu is an Assistant Professor at the University of Massachusetts Amherst, USA. His research areas include the application of deep learning in solving partial differential equations, statistical dynamics, and neural network generalization. Recent publications reflect his expertise in generative modeling, approximation theory, and the analysis of Schrödinger equations.

Articles (13)

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2024

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2024

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2023

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2023

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2023

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2022

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2022

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2020

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2020

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2020

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2020

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2020

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2020

Collaborators (3)

Lihan Wang

Assistant Professor

National University of Singapore

SINGAPORE

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Dejan Slepcev

Professor

Carnegie Mellon University

UNITED STATES

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Yahong Yang

Visiting Assistant Professor

Georgia Institute of Technology

UNITED STATES

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Yulong Lu

Research Interests

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About

Articles (13)

Near-optimal deep neural network approximation for Korobov functions with respect to Lp and H1 norms

Generative Downscaling of PDE Solvers with Physics-Guided Diffusion Models

A REGULARITY THEORY FOR STATIC SCHRÖDINGER EQUATIONS ON R <sup>d</sup> IN SPECTRAL BARRON SPACES

Birth–death dynamics for sampling: global convergence, approximations and their asymptotics

Transfer Learning Enhanced DeepONet for Long-Time Prediction of Evolution Equations

Solving multiscale steady radiative transfer equation using neural networks with uniform stability

A priori generalization error analysis of two-layer neural networks for solving high dimensional Schrödinger eigenvalue problems

Continuum limit and preconditioned Langevin sampling of the path integral molecular dynamics

Geometric ergodicity of Langevin dynamics with Coulomb interactions

Quantitative Propagation of Chaos in a Bimolecular Chemical Reaction-Diffusion Model

Quantitative propagation of chaos in a bimolecular chemical reaction-diffusion model

Uniform-in-time weak error analysis for stochastic gradient descent algorithms via diffusion approximation

Uniform-in-time weak error analysis for stochastic gradient descent algorithms via diffusion approximation

Collaborators (3)

Lihan Wang

Dejan Slepcev

Yahong Yang

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