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Found 6 papers, 4 papers with code
DEQGAN: Learning the Loss Function for PINNs with Generative Adversarial Networks
Solutions to differential equations are of significant scientific and engineering relevance.
Multi-Task Learning based Convolutional Models with Curriculum Learning for the Anisotropic Reynolds Stress Tensor in Turbulent Duct Flow
The Reynolds-averaged Navier-Stokes (RANS) equations require accurate modeling of the anisotropic Reynolds stress tensor.
Port-Hamiltonian Neural Networks for Learning Explicit Time-Dependent Dynamical Systems
In this study, we address the challenge of learning from such non-autonomous systems by embedding the port-Hamiltonian formalism into neural networks, a versatile framework that can capture energy dissipation and time-dependent control forces.
Unsupervised Learning of Solutions to Differential Equations with Generative Adversarial Networks
This work develops a novel method for solving differential equations with unsupervised neural networks that applies Generative Adversarial Networks (GANs) to \emph{learn the loss function} for optimizing the neural network.
Solving Differential Equations Using Neural Network Solution Bundles
The time evolution of dynamical systems is frequently described by ordinary differential equations (ODEs), which must be solved for given initial conditions.
Hamiltonian neural networks for solving equations of motion
There has been a wave of interest in applying machine learning to study dynamical systems.
