A Rigid-Flexible Coupled Six-Dimensional Force Sensor and Its PINN-Based Decoupling Algorithm. [PDF]
Sensors (Basel)Zhu Y, Xie Z, Lu C, Xi S, Wang X.
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Implementing physics-informed neural networks with deep learning for differential equations. [PDF]
Front Artif IntellEmmert-Streib F +3 more
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GeoFusion-3D: Multi-Scale Geomorphic Feature Fusion for Landslide Scar Detection Using UAV-Mounted LiDAR. [PDF]
Sensors (Basel)Shrivastava A, Gupta S, Obradovic Z.
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A temperature- and impedance-aware LSTM-PINN framework for physically consistent battery SOH prediction. [PDF]
Sci RepKumar PN +5 more
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Data-driven, ML-assisted approaches to problem well-posedness. [PDF]
PNAS NexusBertalan T +5 more
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Convergence and error analysis of PINNs
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 +2 more
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A physics-informed machine learning framework for predicting and mitigating doxorubicin nanocarrier toxicity in normal cells. [PDF]
Sci RepRahdar A, Fathi-Karkan S.
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A Physics Informed Neural Network (PINN) framework for fractional order modeling of Alzheimer's disease. [PDF]
Front NeuroinformMehmood A +5 more
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A physics-informed neural network approach for estimating population-level pharmacokinetic parameters from aggregated concentration data. [PDF]
J Pharmacokinet PharmacodynTsiros P, Minadakis V, Sarimveis H.
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