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Found 7 papers, 0 papers with code
Stochastic approach for elliptic problems in perforated domains
We propose a neural network-based mesh-free approach for perforated domain problems.
Learning In-between Imagery Dynamics via Physical Latent Spaces
We present a framework designed to learn the underlying dynamics between two images observed at consecutive time steps.
An analysis of the derivative-free loss method for solving PDEs
This study analyzes the derivative-free loss method to solve a certain class of elliptic PDEs using neural networks.
Energy-Efficient Driving in Connected Corridors via Minimum Principle Control: Vehicle-in-the-Loop Experimental Verification in Mixed Fleets
Up to 36% fuel savings are measured with the proposed control approach over a human-modelled driver, and it was found connectivity in the automation approach improved fuel economy by up to 26% over automation without.
A Neural Network Approach for Homogenization of Multiscale Problems
Compared with other network-based approaches for multiscale problems, the proposed method is free from the design of hand-crafted neural network architecture and the cell problem to calculate the homogenization coefficient.
Hierarchical Learning to Solve Partial Differential Equations Using Physics-Informed Neural Networks
In this work, we propose a hierarchical approach to improve the convergence rate and accuracy of the neural network solution to partial differential equations.
A Derivative-Free Method for Solving Elliptic Partial Differential Equations with Deep Neural Networks
We introduce a deep neural network based method for solving a class of elliptic partial differential equations.
