The Neuromorphic Element: A Data-Driven Finite Element Formulation Using Self-Designing Neural Networks: Proof of Concept on Nonlinear Trusses

In this thesis, a new data-driven finite element is developed, which is referred to as a neuromorphic element (designated as NmT2). Its goal is to reduce the computational expense of FEA models with- out compromising solution accuracy by embedding a neural network, trained on an element level. The neural network is developed such that the...

Accelerating neural networks embedded on a microcomputer trained on KITTI

Due to a high contribution of human error in fatal traffic accidents, efforts in research and industry for automating vehicles steadily increased the last 3 decades. To reduce accidents with other road users, automated recognition and classification of road users is crucial. The most accurate models are large convolutional neural networks,...