Multi-Agent Reinforcement Learning for Adaptive Mesh Refinement

1 code implementation • 2 Nov 2022 • Jiachen Yang, Ketan Mittal, Tarik Dzanic, Socratis Petrides, Brendan Keith, Brenden Petersen, Daniel Faissol, Robert Anderson

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

no code implementations • 1 Mar 2021 • Jiachen Yang, Tarik Dzanic, Brenden Petersen, Jun Kudo, Ketan Mittal, Vladimir Tomov, Jean-Sylvain Camier, Tuo Zhao, Hongyuan Zha, Tzanio Kolev, Robert Anderson, Daniel Faissol

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.

Inductive Bias reinforcement-learning +1

Recurring Concept Meta-learning for Evolving Data Streams

no code implementations • 21 May 2019 • Robert Anderson, Yun Sing Koh, Gillian Dobbie, Albert Bifet

The novelty of ECPF is in how it uses similarity of classifications on new data, between a new classifier and existing classifiers, to quickly identify the best classifier to reuse.

General Classification Meta-Learning

Expressive Visual Text-to-Speech Using Active Appearance Models

no code implementations • CVPR 2013 • Robert Anderson, Bjorn Stenger, Vincent Wan, Roberto Cipolla

This paper presents a complete system for expressive visual text-to-speech (VTTS), which is capable of producing expressive output, in the form of a 'talking head', given an input text and a set of continuous expression weights.