2020

Teaching Machines to Understand Urban Networks: A Graph Autoencoder Approach

Maria Coelho, Mark Austin, Shivam Mishra, Mark Blackburn

Due to remarkable advances in computer, communications and sensing technologies over the past three decades,large-scale urban systems are now far more heterogeneous and automated than their predecessors. They may, in fact, be connected to other types of systems in completely new ways. These characteristics make the tasks of system design, analysis and integration of multi-disciplinary concerns much more difficult than in the past. We believe these challenges can be addressed by teaching machines to understand urban networks. This paper explores opportunities for using a recently developed graph autoencoding approach to encode the structure and associated network attributes as low-dimensional vectors. We exercise the proposed approach on a problem involving identification of leaks in urban water distribution systems.

IARIA International Journal on Advances in Networks and Services

Teaching Machines to Understand Urban Networks

Maria Coelho, Mark Austin

Explores opportunities for using recently developed graph embedding procedures to encode the structure and associated network attributes as low-dimensional vectors. The AI/ML concept is demonstrated on a problem involving identification of leaks in an urban water distribution system.

15th International Conference on Systems (ICONS 2020)

Architecting Smart City Digital Twins: Combined Semantic Model and Machine Learning Approach

Mark Austin, Parastoo Delgoshaei, Maria Coelho, Mohammad Heidarinejad

Explores the approaches and challenges of architecting and operating smart city digital twins, and proposes a path that supports semantic knowledge representation and reasoning, as well as machine learning formalisms, to provide complementary and supportive roles in collecting and processing data, identifying events, and automating decision making.

Journal of Management in Engineering

2019

Using Semantic Fluency Models Improves Network Reconstruction Engineering Knowledge

Thurston Sexton, Mark Fuge

The paper directly models a cognitive process by which technicians may record work orders, recovering implied engineering knowledge about system structure by processing written records.

ASME 2019 International Design Engineering Technical Conference/Computers and Information in Engineering Conference

Checking the automated construction of finite element simulations from Dirichlet boundary conditions

Keven Chiu, Mark Fuge

From engineering analysis and topology optimization to generative design and machine learning, many modern computational design approaches require either large amounts of data or a method to generate that data. This paper addresses key issues with automatically generating such data through automating the construction of Finite Element Method (FEM) simulations from Dirichlet boundary conditions.

ASME 2019 International Design Engineering Technical Conference/Computers and Information in Engineering Conference