Results 31 to 40 of about 2,291 (214)
Reconstruction of Ship Propeller Wake Field Based on Physics-Informed Neural Networks
Shanghai Jiaotong Daxue xuebaoPhysics-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
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Results in Physics, 2023 In this paper, by modifying loss function MSE and training area of the physics-informed neural network (PINN), we proposed two neural network models: mix-training PINN and prior information mix-training PINN.
Shifang Tian, Chenchen Cao, Biao Li
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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 +8 more
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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 +3 more
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BernoulliPhysics-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 +2 more
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Advanced Science, EarlyView.This review explores the convergence of artificial intelligence technologies in modeling drug–drug and drug–target interactions. By evaluating advanced feature engineering, architectural innovations, and learning paradigms reveals shared evolutionary trends and critical challenges, such as cold‐start settings and shortcut learning.
Xin Sun, Tong Wang
wiley +1 more source
Advanced Science, EarlyView.This perspective proposes a cohesive machine learning strategy to decode microplastic aging. It advocates for Federated Learning to dismantle global data silos and introduces the TRACE framework (TRansport, Aging, Corona, Ecotoxicity). By integrating physics‐informed modeling with causal discovery, this approach bridges the laboratory‐field gap to ...
Yaping Lyu +6 more
wiley +1 more source
AI‐Physics‐Experiment Trinity for Integrated Protein Dynamics Modeling
Advanced Science, EarlyView.This review unites experiments, physics‐based simulations, and AI as a synergistic triad for protein dynamics modeling. It highlights integrative strategies, resolves sampling and forcefield bottlenecks, and outlines challenges and future directions for accurate, interpretable conformational ensemble prediction.
Chen Shi +4 more
wiley +1 more source
Physics‐Informed Neural Network‐Enabled Forward Prediction and Inverse Design of Ring Origami
Advanced Science, EarlyView.This work presents a KRT‐PINN framework that integrates Kirchhoff rod theory with physics‐informed neural networks for the forward prediction and inverse design of ring origami consisting of closed‐loop rods. The framework predicts stable states of segmented rings with prescribed natural‐curvature profiles and determines the natural‐curvature profiles ...
Luyuan Ning +3 more
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