Maximizing Triboelectric Nanogenerators by Physics‐Informed AI Inverse Design
Triboelectric nanogenerators offer an environmentally friendly approach to harvesting
energy from mechanical excitations. This capability has made them widely sought‐after as …
energy from mechanical excitations. This capability has made them widely sought‐after as …
Deep neural operators can predict the real-time response of floating offshore structures under irregular waves
The utilization of neural operators in a digital twin model of an offshore floating structure
holds the potential for a significant shift in the prediction of structural responses and health …
holds the potential for a significant shift in the prediction of structural responses and health …
[HTML][HTML] Data-driven model assessment: A comparative study for ship response determination
Several machine learning approaches to determine ship responses via data-driven models
have been applied. Input features and parameters used relied on time-series analyses …
have been applied. Input features and parameters used relied on time-series analyses …
Prediction modeling for yaw motion of deep-sea mining vehicle during deployment and recovery: A physics informed neural network (PINN) approach
This paper presents a physics informed neural network (PINN) method for constructing a
yaw motion hydrodynamic model of the deep-sea mining vehicle during the deployment and …
yaw motion hydrodynamic model of the deep-sea mining vehicle during the deployment and …
[HTML][HTML] Physics-Informed Neural Networks for Unmanned Aerial Vehicle System Estimation
The dynamic nature of quadrotor flight introduces significant uncertainty in system
parameters, such as thrust and drag factors. Consequently, operators grapple with …
parameters, such as thrust and drag factors. Consequently, operators grapple with …
Motion prediction of semi-submersibles using time-frequency deep-learning model with input of incident waves
Y Li, Y Kou, L **ao, D Li - Ocean Engineering, 2025 - Elsevier
Abstract Machine learning techniques have inspired reduced-order solutions in
hydrodynamic response prediction and hold the potential to provide valuable insights for …
hydrodynamic response prediction and hold the potential to provide valuable insights for …
A Time-Frequency Deep Learning Model for Motion Prediction of Semi-Submersible Using Wave-Excitation Inputs
Y Li, L **ao, M Liu, D Li, KL Li - ISOPE International Ocean and Polar …, 2024 - onepetro.org
The complex motions excited by waves bring challenges to the safety of offshore activities. In
this study, a novel time-frequency deep learning model embedded with the Fourier series …
this study, a novel time-frequency deep learning model embedded with the Fourier series …
Data-Driven Coefficient Estimation for Autonomous Underwater Vehicle Depth Subsystem
Autonomous Underwater Vehicles (AUVs) play a key role in modern marine exploration. The
effective deployment of AUVs relies on accurately determining their dynamics, which can be …
effective deployment of AUVs relies on accurately determining their dynamics, which can be …
[PDF][PDF] Physics-Informed Neural Networks for UAV System Estimation
The dynamic nature of quadrotor flight introduces significant uncertainty in system
parameters, such as thrust and drag factor. Consequently, operators grapple with escalating …
parameters, such as thrust and drag factor. Consequently, operators grapple with escalating …
Neural Network based Extended Kalman Filter for State Estimation of a Furuta Pendulum
The development of neural network based models considering physical constraints offers
some advantages over other models. For instance, they could be trained using small data …
some advantages over other models. For instance, they could be trained using small data …