research
∙
02/03/2023
LSA-PINN: Linear Boundary Connectivity Loss for Solving PDEs on Complex Geometry
We present a novel loss formulation for efficient learning of complex dy...
share
research
∙
02/01/2023
Graph Neural Network Based Surrogate Model of Physics Simulations for Geometry Design
Computational Intelligence (CI) techniques have shown great potential as...
research
∙
12/15/2022
Neuroevolution Surpasses Stochastic Gradient Descent for Physics-Informed Neural Networks
The potential of learned models for fundamental scientific research and ...
research
∙
11/24/2022
Design of Turing Systems with Physics-Informed Neural Networks
Reaction-diffusion (Turing) systems are fundamental to the formation of ...
research
∙
11/22/2022
Robustness of Physics-Informed Neural Networks to Noise in Sensor Data
Physics-Informed Neural Networks (PINNs) have been shown to be an effect...
research
∙
11/22/2022
FastFlow: AI for Fast Urban Wind Velocity Prediction
Data-driven approaches, including deep learning, have shown great promis...
research
∙
10/29/2021
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 ...
research
∙
09/20/2021
Learning in Sinusoidal Spaces with Physics-Informed Neural Networks
A physics-informed neural network (PINN) uses physics-augmented loss fun...
research
∙
05/05/2021
Improved Surrogate Modeling of Fluid Dynamics with Physics-Informed Neural Networks
Physics-Informed Neural Networks (PINNs) have recently shown great promi...
research
∙
01/06/2021
