We propose a hybrid framework opPINN: physics-informed neural network (P...
Physics-Informed Neural Networks (PINNs) has become a prominent applicat...
Learning mapping between two function spaces has attracted considerable
...
The modeling and control of complex physical dynamics are essential in
r...
Solutions of certain partial differential equations (PDEs) are often
rep...
Prompt severity assessment model of confirmed patients who were infected...
In this paper, we propose a novel conservative formulation for solving
k...
Active inference may be defined as Bayesian modeling of a brain with a
b...
Approximating the numerical solutions of Partial Differential Equations
...
This paper focuses on how to approximate traveling wave solutions for va...
The model reduction of a mesoscopic kinetic dynamics to a macroscopic
co...
Steemit is a blockchain-based social media platform, where authors can g...
The issue of the relaxation to equilibrium has been at the core of the
k...
Reinforcement learning with complex tasks is a challenging problem. Ofte...
Learning graph-structured data with graph neural networks (GNNs) has bee...
In this paper, we construct approximated solutions of Differential Equat...
Wasserstein GAN(WGAN) is a model that minimizes the Wasserstein distance...