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Physics-informed neural networks with caputo-fabrizio derivatives for nonlinear fractal-fractional delay equations and chaotic systems [PDF]
Scientific ReportsComplex dynamical systems, ranging from anomalous diffusion and memristor circuits to convective fluid motion in rotating cavities, exhibit non-local memory, long-range interactions, and proportional delays governed by Fractal-Fractional Differential ...
Hafsa Khan +4 more
doaj +2 more sources
LubricantsEnsuring precise prediction, monitoring, and control of frictional contact temperature is imperative for the design and operation of advanced equipment.
Yonggang Meng, Yichun Xia
exaly +3 more sources
IEEE AccessThis work explores the use of Physics-Informed Neural Networks (PINNs) and a newly proposed approach, called the STacked Adaptive Residual PINN (STAR-PINN), to solve magnetic diffusion problems in the magneto quasi static regime.
Alessandro Formisano +2 more
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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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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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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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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SLAS Discovery, 2023 Human induced pluripotent stem cell (iPSC)-derived neurons are being increasingly used for high content imaging and screening. However, iPSC-derived neuronal differentiation and maturation is time-intensive, often requiring >8 weeks.
Elizabeth R. Sharlow +6 more
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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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