Search Results for author:
Found 4 papers, 0 papers with code
A novel corrective-source term approach to modeling unknown physics in aluminum extraction process
With the ever-increasing availability of data, there has been an explosion of interest in applying modern machine learning methods to fields such as modeling and control.
Physics guided neural networks for modelling of non-linear dynamics
The success of the current wave of artificial intelligence can be partly attributed to deep neural networks, which have proven to be very effective in learning complex patterns from large datasets with minimal human intervention.
Taming an autonomous surface vehicle for path following and collision avoidance using deep reinforcement learning
In this article, we explore the feasibility of applying proximal policy optimization, a state-of-the-art deep reinforcement learning algorithm for continuous control tasks, on the dual-objective problem of controlling an underactuated autonomous surface vehicle to follow an a priori known path while avoiding collisions with non-moving obstacles along the way.
Dissecting Deep Neural Networks
It has been shown that neural networks with piecewise affine activation functions are themselves piecewise affine, with their domains consisting of a vast number of linear regions.
