Add to ORKGCompared with conventional numerical approaches to solving partial differential equations (PDEs), physics-informed neural networks (PINN) have manifested the capability to save development effort and computational cost, especially in scenarios of reconstructing the physics field and solving the inverse problem. Considering the advantages of parameter sharing, spatial feature extraction and low inference cost, convolutional neural networks (CNN) are increasingly used in PINN. However, some challenges still remain as follows. To adapt convolutional PINN to solve different PDEs, considerable effort is usually needed for tuning critical hyperparameters. Furthermore, the effects of the finite difference accuracy, and the mesh resolution on the p...
In this paper, we propose physics-informed neural operators (PINO) that combine training data and ph...
In this study, novel physics-informed neural network (PINN) methods for coupling neighboring support...
Physics-informed neural networks (PINNs) have become popular as part of the rapidly expanding deep l...
The physics informed neural network (PINN) is evolving as a viable method to solve partial different...
Physics-Informed Neural Networks (PINN) are neural networks (NNs) that encode model equations, like ...
Physics-Informed Neural Networks (PINN) are neural networks (NNs) that encode model equations, like ...
Neural networks can be trained to solve partial differential equations (PDEs) by using the PDE resid...
Physics informed neural networks (PINNs) have emerged as a powerful tool to provide robust and accur...
Physics-informed neural networks (PINNs) are revolutionizing science and engineering practice by bri...
Physics-Informed Neural Networks (PINNs) are a new class of numerical methods for solving partial di...
We present FO-PINNs, physics-informed neural networks that are trained using the first-order formula...
Physics-Informed Neural Networks (PINN) are neural networks encoding the problem governing equations...
Physics-informed neural networks (PINNs) have emerged as new data-driven PDE solvers for both forwar...
Though PINNs (physics-informed neural networks) are now deemed as a complement to traditional CFD (c...
Physics-informed neural networks (PINNs) have been proposed to learn the solution of partial differe...
In this paper, we propose physics-informed neural operators (PINO) that combine training data and ph...
In this study, novel physics-informed neural network (PINN) methods for coupling neighboring support...
Physics-informed neural networks (PINNs) have become popular as part of the rapidly expanding deep l...
The physics informed neural network (PINN) is evolving as a viable method to solve partial different...
Physics-Informed Neural Networks (PINN) are neural networks (NNs) that encode model equations, like ...
Physics-Informed Neural Networks (PINN) are neural networks (NNs) that encode model equations, like ...
Neural networks can be trained to solve partial differential equations (PDEs) by using the PDE resid...
Physics informed neural networks (PINNs) have emerged as a powerful tool to provide robust and accur...
Physics-informed neural networks (PINNs) are revolutionizing science and engineering practice by bri...
Physics-Informed Neural Networks (PINNs) are a new class of numerical methods for solving partial di...
We present FO-PINNs, physics-informed neural networks that are trained using the first-order formula...
Physics-Informed Neural Networks (PINN) are neural networks encoding the problem governing equations...
Physics-informed neural networks (PINNs) have emerged as new data-driven PDE solvers for both forwar...
Though PINNs (physics-informed neural networks) are now deemed as a complement to traditional CFD (c...
Physics-informed neural networks (PINNs) have been proposed to learn the solution of partial differe...
In this paper, we propose physics-informed neural operators (PINO) that combine training data and ph...
In this study, novel physics-informed neural network (PINN) methods for coupling neighboring support...
Physics-informed neural networks (PINNs) have become popular as part of the rapidly expanding deep l...