- Optimal Network Topology for Responsive Collective Behavior
Mateo D, Horsevad N, Hassani V, Chamanbaz M and Bouffanais R
Science Advances 5, eaau0999, 2019. [pdf] [doi]
From Senseless Swarms to Smart Mobs: Tuning Networks for Prosocial Behavior
Lim SS and Bouffanais R
IEEE Technology and Society Magazine 38(4), 17-19, 2019. [pdf] [doi]
- Data assimilation method to de-noise and de-filter particle image velocimetry data
Gillissen JJJ, Bouffanais R and Yue DKP
J. Fluid Mech. 877, 196-213, 2019. [pdf] [doi]
- Hydrodynamic object identification with artificial neural models
Lakkam S, Balamurali BT and Bouffanais R
Scientific Reports 9, 11242, 2019. [pdf] [doi]
- Self-organizing maps for storage and transfer of knowledge in reinforcement learning
Karimpanal TG and Bouffanais R
Adaptive Behavior 27, 111-126, 2019. [pdf] [doi]
- Decentralized Multi-Floor Exploration by a Swarm of Miniature Robots Teaming with Wall-Climbing Units (Received Outstanding Paper Award)
Kit JL, Dharmawan AG, Mateo D, Foong S, Soh GS, Bouffanais R and Wood KL
IEEE 2019 International Symposium on Multi-Robot and Multi-Agent Systems (MRS), New Brunswick, NJ, 195-201, 2019. [pdf] [doi]
- Robust Stabilization of Resource Limited Networked Control Systems Under Denial-of- Service Attack
Tripathy NS, Chamanbaz M and Bouffanais R
IEEE 58th Annual Conference on Decision and Control (CDC), Nice, France, 1-6, 2019. [pdf]
- Design and Analysis of A Miniature Two-Wheg Climbing Robot with Robust Internal and External Transitioning Capabilities
Koh DCY, Dharmawan AG, Hariri HH, Soh GS, Foong S, Bouffanais R, Low HY and Wood KL
2019 International Conference on Robotics and Automation (ICRA), Montréal, QC, 9740-9746, 2019. [pdf] [doi]
The Applied Complexity Group (ACG), directed by Professor Roland Bouffanais, conducts interdisciplinary research at the interface between Complexity Science, Artificial Intelligence and Engineering Design.
Our research involves a synergistic combination of theoretical and computational developments, with real-life experimental validations.
We foster cross-disciplinary exploration to promote innovative engineering designs and to facilitate the advancement of scientific know-how towards applications. We maintain a constructive and open dialogue between science, industry and society.
Our team members hail from various fields and have expertise in a vast range of disciplines – including machine learning, network science, robotics, hydrodynamics, control theory, and engineering design.
A significant part of our funding comes from industry collaborations, with local industry or government agencies (Defense Science Organization, URA, HDB, MND), as well as multi-national engineering companies (e.g., Airbus, EDF, Thales, etc.).