1 citations · 3 across the 7 of their papers we have counts for
7 papers
Efficient Training of Transfer Mapping in Physics-Infused Machine Learning Models of UAV Acoustic Field
Rayhaan Iqbal, Amir Behjat, Revant Adlakha +3
Physics-Infused Machine Learning (PIML) architectures aim at integrating machine learning with computationally-efficient, low-fidelity (partial) physics models, leading to improved…
Learning Robot Swarm Tactics over Complex Adversarial Environments
Amir Behjat, Hemanth Manjunatha, Prajit KrisshnaKumar +8
To accomplish complex swarm robotic missions in the real world, one needs to plan and execute a combination of single robot behaviors, group primitives such as task allocation, pat…
Efficient Inverse Design of 2D Elastic Metamaterial Systems Using Invertible Neural Networks
Manaswin Oddiraju, Amir Behjat, Mostafa Nouh +1
Locally resonant elastic metamaterials (LREM) can be designed, by optimizing the geometry of the constituent self-repeating unit cells, to potentially damp out vibration in selecte…
Metamodel Based Forward and Inverse Design for Passive Vibration Suppression
Amir Behjat, Manaswin Oddiraju, Mohammad Ali Attarzadeh +2
Aperiodic metamaterials represent a class of structural systems that are composed of different building blocks (cells), instead of a self-repeating chain of the same unit cells. Op…
Training Detection-Range-Frugal Cooperative Collision Avoidance Models for Quadcopters via Neuroevolution
Amir Behjat, Krushang Gabani, Souma Chowdhury
Cooperative autonomous approaches to avoiding collisions among small Unmanned Aerial Vehicles (UAVs) is central to safe integration of UAVs within the civilian airspace. One potent…
Adaptive Genomic Evolution of Neural Network Topologies (AGENT) for State-to-Action Mapping in Autonomous Agents
Amir Behjat, Sharat Chidambaran, Souma Chowdhury
Neuroevolution is a process of training neural networks (NN) through an evolutionary algorithm, usually to serve as a state-to-action mapping model in control or reinforcement lear…