{"id":527,"date":"2020-05-08T09:39:42","date_gmt":"2020-05-08T13:39:42","guid":{"rendered":"https:\/\/carleton.ca\/mdl\/?p=527"},"modified":"2020-05-08T09:39:42","modified_gmt":"2020-05-08T13:39:42","slug":"new-publication-ieee-access","status":"publish","type":"post","link":"https:\/\/carleton.ca\/mdl\/2020\/new-publication-ieee-access\/","title":{"rendered":"New Publication – IEEE Access"},"content":{"rendered":"

We have a new publication:<\/p>\n

Lloyd S., Irani R. A. and Ahmadi M., (2020)\u00a0Neural Network Quadrature for Fast Numerical Integration and Optimization<\/a> in IEEE Access.<\/p>\n

The paper is available through an Early Access release on IEEE Xplore.<\/p>\n

Abstract:<\/strong>
\nWe present a novel numerical integration technique, Neural Network Integration, or NNI, where shallow neural network design is used to approximate an integrand function within a bounded set. This function approximation is such that a closed-form solution exists to its definite integral across any generalized polyhedron within the network\u2019s domain. This closed-form solution allows for fast integral evaluation of the function across different bounds, following the initial training of the network. In other words, it becomes possible to \u201cpre-compute\u201d the numerical integration problem, allowing for rapid evaluation later. Experimental tests are performed using the Genz integration test functions. These experiments show NNI to be a viable integration method, working best on predictable integrand functions, but worse results on singular and non-smooth functions. NNI is proposed as a solution to problems where numerical integrations of higher dimension must be performed over different domains frequently or rapidly and with low memory requirements, such as in real-time or embedded engineering applications. The application of this method to the optimization of integral functions is also discussed.<\/p>\n

<\/p>\n

You can also see our other publications here<\/a>.<\/p>\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

We have a new publication: Lloyd S., Irani R. A. and Ahmadi M., (2020)\u00a0Neural Network Quadrature for Fast Numerical Integration and Optimization in IEEE Access. The paper is available through an Early Access release on IEEE Xplore. Abstract: We present a novel numerical integration technique, Neural Network Integration, or NNI, where shallow neural network design […]<\/p>\n","protected":false},"author":6,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_relevanssi_hide_post":"","_relevanssi_hide_content":"","_relevanssi_pin_for_all":"","_relevanssi_pin_keywords":"","_relevanssi_unpin_keywords":"","_relevanssi_related_keywords":"","_relevanssi_related_include_ids":"","_relevanssi_related_exclude_ids":"","_relevanssi_related_no_append":"","_relevanssi_related_not_related":"","_relevanssi_related_posts":"","_relevanssi_noindex_reason":"","_mi_skip_tracking":false,"_exactmetrics_sitenote_active":false,"_exactmetrics_sitenote_note":"","_exactmetrics_sitenote_category":0,"footnotes":"","_links_to":"","_links_to_target":""},"categories":[20],"tags":[],"yoast_head":"\nNew Publication - IEEE Access - Multi-Domain Laboratory<\/title>\n<meta name=\"description\" content=\"We have a new publication: Lloyd S., Irani R. 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Previously, he was a Senior Mechanical Engineer with Rolls-Royce Canada Limited (Naval Marine). He completed PhD and MASc in Mechanical Engineering at Dalhousie University and his BASc in Mechanical Engineering at the University of Windsor. He is multidisciplinary system modeller who has been published in the Journal of Terramechanics, Aerosol Science and Technology and the Journal of Machine Tools and Manufacture. In the past he has worked with WHOI, MDA Space Mission, DaimlerChrysler and Siemens Automotive. His current research focuses on multi-domain modelling and mechatronic applications for advanced and automated launch and recovery system in the marine environment.\",\"sameAs\":[\"http:\/\/www.carleton.ca\/mdl\"]}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"New Publication - IEEE Access - Multi-Domain Laboratory","description":"We have a new publication: Lloyd S., Irani R. 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