Lu Lu

Assistant Professor

Yale University

United States

Research Interests

Explore related searches

Professors in United States Professors in Fluid Mechanics Fluid Mechanics professors in United States

Contact this professor

Get a free first email built on one of their papers, ready for you to edit and send yourself.

About

Lu Lu is an Assistant Professor at Yale University in the United States. His research interests include one-shot learning, neural topology optimization, and quantum computing applications in deep learning. He has published recent articles on topics such as physics-informed neural networks, dynamic macro-finance models, and machine learning for scientific applications.

Articles (28)

Year:

2025

Year:

2025

Year:

2025

Year:

2025

Year:

2025

Year:

2025

Year:

2025

Year:

2025

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2023

Year:

2023

Year:

2023

Year:

2023

Year:

2023

Year:

2023

Year:

2023

Year:

2022

Year:

2022

Year:

2022

Collaborators (12)

Aleksander Popel

Professor

Johns Hopkins University

UNITED STATES

View details

zhongqiang zhang

Associate Professor

Worcester Polytechnic Institute

UNITED STATES

View details

Ruobing Dong

University of Victoria

CANADA

View details

Kwang Moo Yi

Adjunct Professor

University of Victoria

CANADA

View details

Paris Perdikaris

University of Pennsylvania

UNITED STATES

View details

Anran Jiao

Yale University

UNITED STATES

View details

Xiu Yang

Lehigh University

UNITED STATES

View details

Jian-Xun Wang

Assistant Professor

University of Notre Dame

UNITED STATES

View details

Wensi Wu

Research Assistant Professor

Children's Hospital of Philadelphia

UNITED STATES

View details

LEI XING

Lecturer

University of Surrey

UNITED KINGDOM

View details

WENHAO LI

University of Southern California

UNITED STATES

View details

Yeonjong Shin

Assistant Professor

North Carolina State University

UNITED STATES

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Lu Lu

Research Interests

Explore related searches

Contact this professor

About

Articles (28)

One-shot learning for solution operators of partial differential equations

Quantum DeepONet: Neural operators accelerated by quantum computing

A noninvasive method for determining elastic parameters of valve tissue using physics-informed neural networks

Neural Topology Optimization Via Active Learning for Efficient Channel Design in Turbulent Mass Transfer

Rapid Aerodynamic Prediction of Swept Wings via Physics-Embedded Transfer Learning

Generalized Domain Prompt Learning for Accessible Scientific Vision-Language Models

Conformalized-DeepONet: A distribution-free framework for uncertainty quantification in deep operator networks

Federated Scientific Machine Learning for Approximating Functions and Solving Differential Equations With Data Heterogeneity

Multi-resolution partial differential equations preserved learning framework for spatiotemporal dynamics

Signaling-biophysical modeling unravels mechanistic control of red blood cell phagocytosis by macrophages in sickle cell disease

Fourier-MIONet: Fourier-enhanced multiple-input neural operators for multiphase modeling of geological carbon sequestration

Deep Learning for Solving and Estimating Dynamic Macro-finance Models

D2NO: Efficient handling of heterogeneous input function spaces with distributed deep neural operators

Identifying Heterogeneous Micromechanical Properties of Biological Tissues via Physics‐Informed Neural Networks

Speeding up and reducing memory usage for scientific machine learning via mixed precision

Transferable machine learning model for the aerodynamic prediction of swept wings

PCDMD: Physics-constrained dynamic mode decomposition for accurate and robust forecasting of dynamical systems with imperfect data and physics

Transfer learning on physics-informed neural networks for tracking the hemodynamics in the evolving false lumen of dissected aorta

PPDONet: Deep Operator Networks for Fast Prediction of Steady-state Solutions in Disk–Planet Systems

Integration of multi-physics and machine learning-based surrogate modelling approaches for multi-objective optimization of deformed GDL of PEM fuel cells

Fourier-DeepONet: Fourier-enhanced deep operator networks for full waveform inversion with improved accuracy, generalizability, and robustness

Effective data sampling strategies and boundary condition constraints of physics-informed neural networks for identifying material properties in solid mechanics

Reliable extrapolation of deep neural operators informed by physics or sparse observations

Neural operator prediction of linear instability waves in high-speed boundary layers

A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks

MIONet: Learning Multiple-Input Operators via Tensor Product

Approximation rates of DeepONets for learning operators arising from advection–diffusion equations

Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of nanoscale heat transport

Collaborators (12)

Aleksander Popel

zhongqiang zhang

Ruobing Dong

Kwang Moo Yi

Paris Perdikaris

Anran Jiao

Xiu Yang

Jian-Xun Wang

Wensi Wu

LEI XING

WENHAO LI

Yeonjong Shin

Make your next move.