A Physics-Informed Neural Network approach for compartmental epidemiological models. [PDF]
PLoS Comput BiolMillevoi C, Pasetto D, Ferronato M.
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Rapid estimation of left ventricular contractility with a physics-informed neural network inverse modeling approach. [PDF]
Artif Intell MedNaghavi E +6 more
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Assessment of Spatially-Varying Arterial Wall Stiffness and Pressure Using a Physics-Informed Neural Network and Pulse Wave Imaging: An in Silico and Experimental Phantom Study of Stenotic Vessels. [PDF]
Ultrasound Med BiolRoy T +4 more
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Scientific Machine Learning Through Physics–Informed Neural Networks: Where We Are and What’s Next
Journal of Scientific Computing, 2022 Physics-Informed Neural Networks (PINN) are neural networks (NNs) that encode model equations, like Partial Differential Equations (PDE), as a component of the neural network itself.
Fabio Giampaolo +2 more
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Physics-Informed Neural Networks
2021Physics-informed neural networks (PINNs) are used for problems where data are scarce. The underlying physics is enforced via the governing differential equation, including the residual in the cost function. PINNs can be used for both solving and discovering differential equations.
Stefan Kollmannsberger +3 more
openaire +1 more source
Self-adaptive physics-informed neural networks
Journal of Computational Physics, 2022Levi D. McClenny, Ulisses M. Braga-Neto
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Hp-VPINNs: Variational Physics-Informed Neural Networks With Domain Decomposition
Computer Methods in Applied Mechanics and Engineering, 2021Zhongqiang Zhang +2 more
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Gradient-Enhanced Physics-Informed Neural Networks for Forward and Inverse PDE Problems
Computer Methods in Applied Mechanics and Engineering, 2022Lu Lu, George Karniadakis, Xuhui Meng
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Self-Adaptive Physics-Informed Neural Networks
Journal of Computational Physics, 2023Braga-Neto, Ulisses
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