Results 41 to 50 of about 8,116 (238)

Push the Limits: Human‐Like Multimodal Extreme Embodied Intelligence for Autonomous Racing

Advanced Robotics Research, EarlyView.
Autonomous racing tests embodied intelligence under extreme control limits of speed, actuation, and decision‐making. Inspired by human multimodality, we present Apex, an embodied agent deployed on a super‐formula racing car at Yas Marina. Integrating physics‐informed perception, multimodal learning, and tasking, Apex cuts lap times by 15% and achieves ...
Yanxin Zhou, Baichuan Lou, Wenhui Huang, Binbin Hu, Haohan Yang, Haochen Liu, Xiangkun He, Chee Kiong Ong, Yiran Zhang, Tianchu Su, Bolin Zhao, Xiaoyu Mo, Holden King Ho Li, Chen Lv   +13 more
wiley   +1 more source

A robust Gated-PINN to resolve local minima issues in solving differential algebraic equations

Results in Engineering
Physics-Informed Neural Network (PINN) has emerged as a promising tool for solving various physical problems with differential equations. However in practice, PINN often suffers from the local minima issue while solving problems with minimal initial ...
SangJoon Lee, Byung-Tak Lee, Seok Kap Ko
doaj   +1 more source

Physics-informed neural network with transfer learning (TL-PINN) based on domain similarity measure for prediction of nuclear reactor transients

Scientific Reports, 2023
Nuclear reactor safety and efficiency can be enhanced through the development of accurate and fast methods for prediction of reactor transient (RT) states.
Konstantinos Prantikos, Stylianos Chatzidakis, Lefteri H. Tsoukalas, Alexander Heifetz   +3 more
doaj   +1 more source

A Physics-Informed Machine Learning Approach for Estimating Lithium-Ion Battery Temperature

IEEE Access, 2022
The physics-informed neural network (PINN) has drawn much attention as it can reduce training data size and eliminate the need for physics equation identification.
Gyouho Cho, Mengqi Wang, Youngki Kim, Jaerock Kwon, Wencong Su   +4 more
doaj   +1 more source

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

Advanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu, Tianshuo Wei, Yan Ge, Bo Peng, Yue Li, Maolin Wang, Peng Wen, Chao Yang, Ye Wei   +8 more
wiley   +1 more source

Avancerad PINN-integration med flera PINN-metoder

This thesis evaluates the efficacy of Physics-Informed Neural Networks (PINNs) in simulating fluid dynamics challenges, focusing on the Burgers' equation and the lid-driven cavity problem, to develop a robust PINN framework for nuclear engineering ...
Kang, Hanseul
core   +3 more sources

Reconstruction of Ship Propeller Wake Field Based on Physics-Informed Neural Networks

Shanghai Jiaotong Daxue xuebao
Physics-informed neural networks (PINN) are applied to the reconstruction of the ship propeller wake field. First, the principle and basic framework of PINN were introduced.
HOU Xianrui, ZHOU Xingyu, HUANG Xiaocheng
doaj   +1 more source

An Integrated Artificial Intelligence Framework for Nanomedicine Engineering

Advanced Science, EarlyView.
This review outlines a closed‐loop AI ecosystem for nanomedicine engineering. By synergizing multimodal data harmonization, objective‐driven AI architectures, and automated high‐throughput validation, this “Design‐Make‐Test‐Analyze” (DMTA) workflow transforms traditional heuristic formulation into a predictive, self‐optimizing paradigm, accelerating ...
Meichen Dong, Qin Luo, Xinyi Ding, Chaoqi Lu, Ru Zhang, Qingqing Tan, Zhiqing Pang, Defang Ouyang, Dongsheng Li, Jiaxuan Xia, Jianxin Wang   +10 more
wiley   +1 more source

Data‐Driven and Physics‐Informed Modeling Perspectives for Soft Manipulators: From SINDy to Hybrid Intelligence

Advanced Science, EarlyView.
This Perspective charts the modeling landscape for soft manipulators, spanning sparse system identification, neural dynamics, physics‐informed models and hybrid intelligence. We examine how physics, data, sensing, adaptation and control can assume complementary roles within this landscape, and provide a framework for selecting modeling approaches ...
Wenbo Hui, Yunyi Wang, Teng Zhang, Yang Chen, Min Pan   +4 more
wiley   +1 more source

On the convergence of PINNs

Bernoulli
Physics-informed neural networks (PINNs) are a promising approach that combines the power of neural networks with the interpretability of physical modeling. PINNs have shown good practical performance in solving partial differential equations (PDEs) and in hybrid modeling scenarios, where physical models enhance data-driven approaches.
Doumèche, Nathan, Biau, Gérard, Boyer, Claire   +2 more
openaire   +4 more sources