114 citations · 165 across the 3 of their papers we have counts for
4 papers · 1 filter
Physics-informed attention-based neural network for solving non-linear partial differential equations
Ruben Rodriguez-Torrado, Pablo Ruiz, Luis Cueto-Felgueroso +4
Physics-Informed Neural Networks (PINNs) have enabled significant improvements in modelling physical processes described by partial differential equations (PDEs). PINNs are based o…
Accelerating Physics-Based Simulations Using Neural Network Proxies: An Application in Oil Reservoir Modeling
Jiri Navratil, Alan King, Jesus Rios +3
We develop a proxy model based on deep learning methods to accelerate the simulations of oil reservoirs--by three orders of magnitude--compared to industry-strength physics-based P…
Deep Reinforcement Learning for General Video Game AI
Ruben Rodriguez Torrado, Philip Bontrager, Julian Togelius +2
The General Video Game AI (GVGAI) competition and its associated software framework provides a way of benchmarking AI algorithms on a large number of games written in a domain-spec…
Illuminating Generalization in Deep Reinforcement Learning through Procedural Level Generation
Niels Justesen, Ruben Rodriguez Torrado, Philip Bontrager +3
Deep reinforcement learning (RL) has shown impressive results in a variety of domains, learning directly from high-dimensional sensory streams. However, when neural networks are tr…