Results 271 to 280 of about 29,946 (302)

A deep learning approach: physics-informed neural networks for solving a nonlinear telegraph equation with different boundary conditions. [PDF]

BMC Res Notes
Deresse AT, Bekela AS.
europepmc   +1 more source

Coagulo-Net: Enhancing the mathematical modeling of blood coagulation using physics-informed neural networks. [PDF]

Neural Netw
Qian Y, Zhu G, Zhang Z, Modepalli S, Zheng Y, Zheng X, Frydman G, Li H.   +7 more
europepmc   +1 more source

Physics-informed neural networks with hybrid Kolmogorov-Arnold network and augmented Lagrangian function for solving partial differential equations. [PDF]

Sci Rep
Zhang Z, Wang Q, Zhang Y, Shen T, Shen T, Zhang W.   +5 more
europepmc   +1 more source

Identifying Heterogeneous Micromechanical Properties of Biological Tissues via Physics-Informed Neural Networks. [PDF]

Small Methods
Wu W, Daneker M, Turner KT, Jolley MA, Lu L.   +4 more
europepmc   +1 more source

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Physics-Informed Neural Networks

2021
Physics-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, Davide D’Angella, Moritz Jokeit, Leon Herrmann   +3 more
openaire   +1 more source

Physics-informed neural networks for tsunami inundation modeling

Journal of Computational Physics
We use physics-informed neural networks for solving the shallow-water equations for tsunami modeling. Physics-informed neural networks are an optimization based approach for solving differential equations that is completely meshless.
Rüdiger Brecht, Alexander Bihlo
exaly   +2 more sources

Self-adaptive physics-informed neural networks

Journal of Computational Physics, 2022
Levi D. McClenny, Ulisses M. Braga-Neto
openaire   +1 more source

Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next

Journal of Scientific Computing, 2022
Salvatore Cuomo, Vincenzo Schiano Di Cola, Fabio Giampaolo   +2 more
exaly  

hp-VPINNs: Variational physics-informed neural networks with domain decomposition

Computer Methods in Applied Mechanics and Engineering, 2021
Ehsan Kharazmi, Zhongqiang Zhang, George Em Karniadakis   +2 more
exaly  

Outlier-resistant physics-informed neural network

Physical Review E
Recent advances in machine learning have introduced physics-informed neural networks (PINN) as a valuable tool for addressing dynamics through governing equations and experimental observations. Outliers can be present in measurements and significantly affect the accuracy of the solutions provided by PINN.
D. H. G. Duarte, P. D. S. de Lima, J. M. de Araújo   +2 more
openaire   +2 more sources