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Physics-informed neural network with weighted loss and hard constraints for hyperbolic conservation laws [PDF]
Scientific ReportsIn this study, we proposed a weighted loss hard constraint physics-informed neural networks (PINNs) called WHC-PINN. WHC-PINN solves hyperbolic equations with the aid of a gradient weighting approach and by applying hard constraints.
Mahshid Sadat Ghoreishi, Hamid Naderan
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LubricantsEnsuring precise prediction, monitoring, and control of frictional contact temperature is imperative for the design and operation of advanced equipment.
Yonggang Meng, Meng Yonggang
exaly +3 more sources
Bio-Protocol, 2023 Live imaging is commonly used to study dynamic processes in cells. Many labs carrying out live imaging in neurons use kymographs as a tool. Kymographs display time-dependent microscope data (time-lapsed images) in two-dimensional representations showing ...
Digilio Laura +4 more
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Fluids, 2023 Machine learning-based modeling of physical systems has attracted significant interest in recent years. Based solely on the underlying physical equations and initial and boundary conditions, these new approaches allow to approximate, for example, the ...
Philipp Moser +4 more
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Cejing jishu, 2023 In order to simulate the response of electromagnetic wave resistivity logging while drilling efficiently in complex media and accelerate the inversion of logging data, the physical-informed neural network (PINN) is used to simulate the response of ...
LIU Yang, WANG Jian, XU Delong
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Energies, 2022 Physics-informed neural network (PINN) models are developed in this work for solving highly anisotropic diffusion equations. Compared to traditional numerical discretization schemes such as the finite volume method and finite element method, PINN models ...
Wenjuan Zhang, Mohammed Al Kobaisi
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On physics-informed neural networks for quantum computers
Frontiers in Applied Mathematics and Statistics, 2022 Physics-Informed Neural Networks (PINN) emerged as a powerful tool for solving scientific computing problems, ranging from the solution of Partial Differential Equations to data assimilation tasks.
Stefano Markidis
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Computation of flow through TAVI device by means of physics informed neural networks
Current Directions in Biomedical Engineering, 2022 Cardiovascular diseases are among the most common diseases with high mortality, including aortic valve stenosis and insufficiency. Minimally invasive implantation of transcatheter aortic valve prosthesis (TAVI) has become the standard procedure for ...
Oldenburg Jan +3 more
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Physics-Informed Neural Networks for Low Reynolds Number Flows over Cylinder
Energies, 2023 Physics-informed neural network (PINN) architectures are recent developments that can act as surrogate models for fluid dynamics in order to reduce computational costs.
Elijah Hao Wei Ang +2 more
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Germline variants predictive of tumor mutational burden and immune checkpoint inhibitor efficacy
iScience, 2021 Summary: High tumor mutational burden (TMB) is associated with response to checkpoint blockade in several cancers. We identify pathogenic germline variants associated with increased TMB (GVITMB).
Ajay Chatrath +2 more
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