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Andrea D?Ambrosio and Roberto Furfaro
This paper demonstrates the utilization of Pontryagin Neural Networks (PoNNs) to acquire control strategies for achieving fuel-optimal trajectories. PoNNs, a subtype of Physics-Informed Neural Networks (PINNs), are tailored for solving optimal control pr...
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Alexander Isaev, Tatiana Dobroserdova, Alexander Danilov and Sergey Simakov
This study introduces an innovative approach leveraging physics-informed neural networks (PINNs) for the efficient computation of blood flows at the boundaries of a four-vessel junction formed by a Fontan procedure. The methodology incorporates a 3D mesh...
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Brett Bowman, Chad Oian, Jason Kurz, Taufiquar Khan, Eddie Gil and Nick Gamez
Modeling of physical processes as partial differential equations (PDEs) is often carried out with computationally expensive numerical solvers. A common, and important, process to model is that of laser interaction with biological tissues. Physics-informe...
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Binghang Lu, Christian Moya and Guang Lin
This paper presents NSGA-PINN, a multi-objective optimization framework for the effective training of physics-informed neural networks (PINNs). The proposed framework uses the non-dominated sorting genetic algorithm (NSGA-II) to enable traditional stocha...
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Zhou Yang, Yuwang Xu, Jionglin Jing, Xuepeng Fu, Bofu Wang, Haojie Ren, Mengmeng Zhang and Tongxiao Sun
Particle image velocimetry (PIV) is a widely used experimental technique in ocean engineering, for instance, to study the vortex fields near marine risers and the wake fields behind wind turbines or ship propellers. However, the flow fields measured usin...
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Zaharaddeen Karami Lawal, Hayati Yassin, Daphne Teck Ching Lai and Azam Che Idris
This research aims to study and assess state-of-the-art physics-informed neural networks (PINNs) from different researchers? perspectives. The PRISMA framework was used for a systematic literature review, and 120 research articles from the computational ...
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Muhammad Usama, Rui Ma, Jason Hart and Mikaela Wojcik
Traffic state estimation (TSE) is a critical component of the efficient intelligent transportation systems (ITS) operations. In the literature, TSE methods are divided into model-driven methods and data-driven methods. Each approach has its limitations. ...
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Peng-Fei Xu, Chen-Bo Han, Hong-Xia Cheng, Chen Cheng and Tong Ge
A three-degrees-of-freedom model, including surge, sway and yaw motion, with differential thrusters is proposed to describe unmanned surface vehicle (USV) dynamics in this study. The experiment is carried out in the Qing Huai River and the data obtained ...
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