Physics-Informed Neural Networks for Unmanned Aerial Vehicle System Estimation by Domenico Bianchi, Nicola Epicoco, Mario Di Ferdinando, Stefano Di Gennaro, Pierdomenico Pepe

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Multifidelity Kolmogorov–Arnold networks by Amanda A Howard, Bruno Jacob, Panos Stinis

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Applications of physics-informed neural networks in geosciences: From basic seismology to comprehensive environmental studies by Habib Maan, Habib Ahed, Alibrahim Bashar

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Enhancing convergence speed with feature enforcing physics-informed neural networks using boundary conditions as prior knowledge by Mahyar Jahani-nasab, Mohamad Ali Bijarchi

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Causality-Aware Training of Physics-Informed Neural Networks for Solving Inverse Problems by Jaeseung Kim, Hwijae Son

Subjects: “…physics-informed neural networks (PINNs)…”
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LatentPINNs: Generative physics-informed neural networks via a latent representation learning by Mohammad H. Taufik, Tariq Alkhalifah

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Meshfree Variational-Physics-Informed Neural Networks (MF-VPINN): An Adaptive Training Strategy by Stefano Berrone, Moreno Pintore

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Benchmark problems for physics-informed neural networks: The Allen–Cahn equation by Hyun Geun Lee, Youngjin Hwang, Yunjae Nam, Sangkwon Kim, Junseok Kim

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Modelling fourth-order hyperelasticity in soft solids using physics informed neural networks without labelled data by Vikrant Pratap, Pratyush Kumar, Chethana Rao, Michael D. Gilchrist, Bharat B. Tripathi

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PINN based on multi-scale strategy for solving Navier–Stokes equation by Shirong Li, Shaoyong Lai

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Coupling of Lagrangian Mechanics and Physics-Informed Neural Networks for the Identification of Migration Dynamics by Kirill Zakharov, Anton Kovantsev, Alexander Boukhanovsky

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Novel Physics-Informed Artificial Neural Network Architectures for System and Input Identification of Structural Dynamics PDEs by Sarvin Moradi, Burak Duran, Saeed Eftekhar Azam, Massood Mofid

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Application of physics-informed neural networks (PINNs) solution to coupled thermal and hydraulic processes in silty sands by Yuan Feng, Jongwan Eun, Seunghee Kim, Yong-Rak Kim

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Physics Informed Neural Networks-Based AC Optimal Power Flow Under High RES Penetration by Fahad Saleh Al-Ismail

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Physics informed neural networks for fluid flow analysis with repetitive parameter initialization by Jongmok Lee, Seungmin Shin, Taewan Kim, Bumsoo Park, Ho Choi, Anna Lee, Minseok Choi, Seungchul Lee

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Thermodynamically consistent modeling of granular soils using physics-informed neural networks by Nazanin Irani, Mohammad Salimi, Torsten Wichtmann

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Deterministic and statistical calibration of constitutive models from full-field data with parametric physics-informed neural networks by David Anton, Jendrik-Alexander Tröger, Henning Wessels, Ulrich Römer, Alexander Henkes, Stefan Hartmann

Subjects: “…Parametric physics-informed neural networks…”
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Physical-informed neural network in modeling and control of a servo mechanical rotor system by Yi-Ho Chen, Chao-Chung Peng

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Physics-Informed Graph Neural Networks for Attack Path Prediction by Marin François, Pierre-Emmanuel Arduin, Myriam Merad

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High-Accuracy Parallel Neural Networks with Hard Constraints for a Mixed Stokes/Darcy Model by Zhulian Lu, Junyang Zhang, Xiaohong Zhu

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