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Python-based command line tool with Fortran routines for global optimization of atomistic arrangement problems by Couolmb energy.

Title: An introduction to training algorithms for neuromorphic computing and on-line learning Abstract: Training neural networks implemented in neuromorphic hardware is challenging due to the dynamic, sparse, and local nature of the computations. This tutorial will describe some established gradient-based solutions to address these challenges in the context of real-valued recurrent neural networks and spiking neural networks. Insights into gradient-based training algorithms and associated autodifferentiation methods lead to online synaptic plasticity rules and the necessary assumptions to implement them in in-memory computing devices. The tutorial will conclude with methods that can be used to improve and optimize learning algorithms using meta-learning and other meta-optimization approaches.

Code for generation & analysis of an AiiDA database of double transition metal impurity embeddings into the topological insulator Bi2Te3.

Fork of https://iffgit.fz-juelich.de/mozumder/Master_Thesis_Mozumder .

Nodes to be shared in IAS-1

Doctoral thesis (dissertation) of J. Wasmer's PhD project "Development of a surrogate machine learning model for the acceleration of first-principles calculations". Monograph.