A hybrid model-data-driven framework for inverse load identification of interval structures based on physics-informed neural network and improved Kalman filter …
Accurately capturing data on the external loads that large structural systems endure is
crucial for improving the performance of energy equipment. This paper introduces a novel …
crucial for improving the performance of energy equipment. This paper introduces a novel …
Three-dimensional spatiotemporal wind field reconstruction based on physics-informed deep learning
J Zhang, X Zhao - Applied Energy, 2021 - Elsevier
In this work, a physics-informed deep learning model is developed to achieve the
reconstruction of the three-dimensional (3-D) spatiotemporal wind field in front of a wind …
reconstruction of the three-dimensional (3-D) spatiotemporal wind field in front of a wind …
[HTML][HTML] 面向流体力学的多范式融合研究展望
张伟伟， 王旭， 寇家庆 - 力学进展, 2023 - lxjz.cstam.org.cn
实验观测, 理论研究以及数值模拟是包括流体力学在内很多学科的基本研究范式. 21 世纪以来,
大数据驱动下的人工智能成为引领新一轮科技革命和产业变革的重要驱动力 …
大数据驱动下的人工智能成为引领新一轮科技革命和产业变革的重要驱动力 …
[HTML][HTML] Prospects of multi-paradigm fusion methods for fluid mechanics research
Experimental observation, theoretical research and numerical simulation are the basic
research paradigms in many disciplines, including fluid mechanics. Since the 21st century …
research paradigms in many disciplines, including fluid mechanics. Since the 21st century …
Wind power prediction based on multi-class autoregressive moving average model with logistic function
The seasonality and randomness of wind present a significant challenge to the operation of
modern power systems with high penetration of wind generation. An effective short-term …
modern power systems with high penetration of wind generation. An effective short-term …
A deep learning method based on partition modeling for reconstructing temperature field
Physical field reconstruction is highly desirable for the measurement and control of
engineering systems. The reconstruction of the temperature field from limited observation …
engineering systems. The reconstruction of the temperature field from limited observation …
Residual-connected physics-informed neural network for anti-noise wind field reconstruction
Physics-informed neural network (PINN)-based methods have recently been applied to
reconstruct the spatiotemporal wind field based on LIDAR measurements. However, the …
reconstruct the spatiotemporal wind field based on LIDAR measurements. However, the …
A hybrid deep learning framework for unsteady periodic flow field reconstruction based on frequency and residual learning
X Peng, X Li, X Chen, X Chen, W Yao - Aerospace Science and …, 2023 - Elsevier
Reconstructing a complete flow field from limited sensor measurement is quite essential for
state evaluation, optimization, monitoring, and control of the flow system. Unsteady periodic …
state evaluation, optimization, monitoring, and control of the flow system. Unsteady periodic …
Parameter optimization of open-loop control of a circular cylinder by simplified reinforcement learning
Open-loop control is commonly considered an efficient approach in flow control, in which the
search for control parameters with excellent performance is mostly carried out by grid …
search for control parameters with excellent performance is mostly carried out by grid …
基于多源数据融合的翼型表面压强精细化重构方法
赵旋， 彭绪浩， 邓子辰， 张伟伟 - 实验流体力学, 2022 - pubs.cstam.org.cn
在风洞试验模型表面布置测压孔是获得表面压力分布的重要手段, 但受限于空间位置和试验成本
, 通常难以在复杂模型表面布置足量的测压孔获得完整的表面压力分布信息 …
, 通常难以在复杂模型表面布置足量的测压孔获得完整的表面压力分布信息 …