Search Results for author:
Found 3 papers, 1 papers with code
Multi-Agent Reinforcement Learning for Adaptive Mesh Refinement
Comprehensive experiments show that VDGN policies significantly outperform error threshold-based policies in global error and cost metrics.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Reinforcement Learning for Adaptive Mesh Refinement
Large-scale finite element simulations of complex physical systems governed by partial differential equations (PDE) crucially depend on adaptive mesh refinement (AMR) to allocate computational budget to regions where higher resolution is required.
Fourier Spectrum Discrepancies in Deep Network Generated Images
Advancements in deep generative models such as generative adversarial networks and variational autoencoders have resulted in the ability to generate realistic images that are visually indistinguishable from real images, which raises concerns about their potential malicious usage.
