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Found 13 papers, 0 papers with code
Neural Operators Learn the Local Physics of Magnetohydrodynamics
Magnetohydrodynamics (MHD) plays a pivotal role in describing the dynamics of plasma and conductive fluids, essential for understanding phenomena such as the structure and evolution of stars and galaxies, and in nuclear fusion for plasma motion through ideal MHD equations.
Approximating Numerical Fluxes Using Fourier Neural Operators for Hyperbolic Conservation Laws
To this end, we developed loss functions inspired by established numerical schemes related to conservation laws and approximated numerical fluxes using Fourier neural operators (FNOs).
An Infinite-Width Analysis on the Jacobian-Regularised Training of a Neural Network
The recent theoretical analysis of deep neural networks in their infinite-width limits has deepened our understanding of initialisation, feature learning, and training of those networks, and brought new practical techniques for finding appropriate hyperparameters, learning network weights, and performing inference.
Virtual Action Actor-Critic Framework for Exploration (Student Abstract)
In this paper, a novel actor-critic framework namely virtual action actor-critic (VAAC), is proposed to address the challenge of efficient exploration in RL.
Enhanced Transformer Architecture for Natural Language Processing
Transformer is a state-of-the-art model in the field of natural language processing (NLP).
Road Redesign Technique Achieving Enhanced Road Safety by Inpainting with a Diffusion Model
Image inpainting is based on inpainting safe roadway elements in a roadway image, replacing accident-prone (AP) features by using a diffusion model.
Off-Policy Reinforcement Learning with Loss Function Weighted by Temporal Difference Error
We propose a novel method that introduces a weighting factor for each experience when calculating the loss function at the learning stage.
Reinforcement Learning for Predicting Traffic Accidents
As the demand for autonomous driving increases, it is paramount to ensure safety.
Kick-motion Training with DQN in AI Soccer Environment
In this paper, we attempt to use the relative coordinate system (RCS) as the state for training kick-motion of robot agent, instead of using the absolute coordinate system (ACS).
Bounding the Rademacher Complexity of Fourier neural operators
Using capacity based on these norms, we bound the generalization error of the model.
Cluster-based Sampling in Hindsight Experience Replay for Robotic Tasks (Student Abstract)
The proposed sampling strategy groups episodes with different achieved goals by using a cluster model and samples experiences in the manner of HER to create the training batch.
Path Planning of Cleaning Robot with Reinforcement Learning
To solve this electricity consumption issue, the problem of efficient path planning for cleaning robot has become important and many studies have been conducted.
Two-stage training algorithm for AI robot soccer
Because these two training processes are conducted in a series in every timestep, agents can learn how to maximize role rewards and team rewards simultaneously.
Multi-agent Reinforcement Learning
reinforcement-learning
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