Junle Liu

Junle Liu 刘军乐

Research Fellow, Passionate about AI & Fluids Engineering 研究员，专注于人工智能与流体工程

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portfolio

publications

PresCast: Physics-Constrained Fourier Kolmogorov-Arnold Networks for Bluff Bodies Spatiotemporal Wall Pressure Forecast PresCast：用于钝体时空壁面压力预测的物理约束 Fourier Kolmogorov-Arnold 网络

Published in NeurIPS25 UrbanAI: Harnessing Artificial Intelligence for Smart Cities, 2024发表于 NeurIPS25 UrbanAI: Harnessing Artificial Intelligence for Smart Cities，2024

This paper is about AI techniques in spatiotemporal prediction

本文研究人工智能技术在时空预测中的应用。

Recommended citation: Liu et al. (2025). "PresCast: Physics-Constrained Fourier Kolmogorov-Arnold Networks for Bluff Bodies Spatiotemporal Wall Pressure Forecast." NeurIPS25 UrbanAI: Harnessing Artificial Intelligence for Smart Cities.
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An aerodynamic database of synchronized surface pressure and flow field for 2D rectangular cylinders 二维矩形柱同步表面压力与流场空气动力学数据库

Published in Engineering Structures, 2025发表于 Engineering Structures，2025

This paper is about the wind tunnel testing and artificial intelligence.

本文研究风洞试验与人工智能方法。

Recommended citation: Liu et al. (2025). "An aerodynamic database of synchronized surface pressure and flow field for 2D rectangular cylinders." Engineering Structures 1. 326, 119506.
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Multi-fidelity Data Reconstruction for Wind Pressure on Building 面向建筑风压的多保真数据重构

Published in NeurIPS25 UrbanAI: Harnessing Artificial Intelligence for Smart Cities, 2025发表于 NeurIPS25 UrbanAI: Harnessing Artificial Intelligence for Smart Cities，2025

This paper is about data fusion with experimental data and numerical data.

本文研究实验数据与数值数据的融合方法。

Recommended citation: Ke et al. (2025). "Multi-fidelity Data Reconstruction for Wind Pressure on Building." NeurIPS25 UrbanAI: Harnessing Artificial Intelligence for Smart Cities.
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Spatiotemporal wall pressure forecast of a rectangular cylinder with physics-aware DeepU-Fourier neural network 基于物理感知 DeepU-Fourier 神经网络的矩形柱时空壁面压力预测

Published in Physics of Fluids, 2025发表于 Physics of Fluids，2025

This paper is about estimating physics-aware AI in forecasting spatiotemporal wall pressure.

本文研究物理感知人工智能在时空壁面压力预测中的应用。

Recommended citation: Liu et al (2025). " Spatiotemporal wall pressure forecast of a rectangular cylinder with physics-aware DeepU-Fourier neural network." Physics of Fluids. 37(12).

Wind energy potential of a novel green building design: Three connected high-rise buildings with Y-plan layout 一种新型绿色建筑设计的风能潜力：三座采用 Y 形平面并相互连接的高层建筑

Published in Energy, 2026发表于 Energy，2026

This paper estimates the wind energy potential of installing wind turbines in urban city buildings

本文评估在城市建筑中安装风力涡轮机的风能潜力。

Recommended citation: Chen et al. (2026). " Wind energy potential of a novel green building design: Three connected high-rise buildings with Y-plan layout;." Energy, 344, 140124.
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talks

Give a Presentation and Visit Northeastern University (China) 访问东北大学并作学术报告

Published:

I am honored to be invited to visit Northwestern University (China) and deliver a presentation on the application of AI in fluid mechanics, and discuss with our colleagues on the perspectives of AI in fluid mechanics.

I am honored to be invited to visit Northwestern University (China) and deliver a presentation on the application of AI in fluid mechanics, and discuss with our colleagues on the perspectives of AI in fluid mechanics.

teaching