Search Results for author:
Found 5 papers, 0 papers with code
LSA-PINN: Linear Boundary Connectivity Loss for Solving PDEs on Complex Geometry
On the other hand, if the samples are too sparse, existing PINNs tend to overfit the near boundary region, leading to incorrect solution.
Neuroevolution of Physics-Informed Neural Nets: Benchmark Problems and Comparative Results
Hence, neuroevolution algorithms, with their superior global search capacity, may be a better choice for PINNs relative to gradient descent methods.
CAN-PINN: A Fast Physics-Informed Neural Network Based on Coupled-Automatic-Numerical Differentiation Method
In this study, novel physics-informed neural network (PINN) methods for coupling neighboring support points and their derivative terms which are obtained by automatic differentiation (AD), are proposed to allow efficient training with improved accuracy.
Learning in Sinusoidal Spaces with Physics-Informed Neural Networks
In this paper, we present a novel perspective of the merits of learning in sinusoidal spaces with PINNs.
Improved Surrogate Modeling of Fluid Dynamics with Physics-Informed Neural Networks
In addition, we propose a novel transfer optimization scheme for use in such surrogate modeling scenarios and demonstrate an approximately 3x improvement in speed to convergence and an order of magnitude improvement in predictive performance over conventional Xavier initialization for training of new scenarios.
