{"id":67,"date":"2020-04-06T13:04:36","date_gmt":"2020-04-06T13:04:36","guid":{"rendered":"https:\/\/ainst.scify.org\/?page_id=67"},"modified":"2024-10-02T10:17:45","modified_gmt":"2024-10-02T10:17:45","slug":"ainst2020","status":"publish","type":"page","link":"https:\/\/ainst.scify.org\/?page_id=67","title":{"rendered":"Program AINST 2024"},"content":{"rendered":"\n
    \n
  1. Molecular Simulation of Coarse-grained Systems using Machine Learning<\/strong>, Dimitrios-Paraskevas Gerakinis, Eleonora Ricci, George Giannakopoulos, Vangelis Karkaletsis, Doros N. Theodorou and Niki Vergadou<\/li>\n\n\n\n
  2. Machine Learning Applications in Nanotechnology Manufacturing: From Etching Accuracy to Deposition Prediction Learning<\/strong>, Alex Kondi, Efi-Maria Papia and Vassilios Constantoudis<\/li>\n\n\n\n
  3. A Mixture Density Network approach in evaluating energy-dependent fission yields Learning<\/strong>, Vaia Prassa and Vasilis Tsioulos<\/li>\n\n\n\n
  4. Self-Adaptive Optimization of Coefficients in Multi-Objective Loss Functions<\/strong>, Spilios Dellis, Eleonora Ricci, Dimitris-Paraskevas Gerakinis, Niki Vergadou and George Giannakopoulos<\/li>\n\n\n\n
  5. Refining Flow Structures with Deep Learning and Super Resolution Methods<\/strong>, Filippos Sofos, George Sofiadis and Antonios Liakopoulos<\/li>\n\n\n\n
  6. Inverse design of Hexagonal Moir\u00e9 Materials: Machine Learning for tunable pore properties<\/strong>, Efi-Maria Papia, Alex Kondi and Vassilios Constantoudis<\/li>\n\n\n\n
  7. TransChem: Effective Pre-training Enhances Molecular Property Prediction<\/strong>, Dimitrios Kelesis, Eirini Spyropoulou and Elias Zavitsanos<\/li>\n\n\n\n
  8. Solving Linear Elasticity Problems using Physics-Informed Neural Networks<\/strong>, Petros Kafkas, George Giannakopoulos and Christoforos Rekatsinas<\/li>\n\n\n\n
  9. Comparing Prior and Learned Time Representations in Transformer Models of Timeseries<\/strong>, Natalia Koliou, Tatiana Boura, Stasinos Konstantopoulos, Georgios Meramveliotakis and George Kosmadakis<\/li>\n\n\n\n
  10. Multi-fidelity Bayesian Optimization for Efficiently Sampling the Design Space of Functionalized Nanoporous Materials<\/strong>, Ioannis Theocharis, Panagiotis Krokidas, Vassilis Gkatsis and George Giannakopoulos<\/li>\n\n\n\n
  11. Enalos Cloud Platform: A User-Oriented Approach to Cheminformatics and Advanced Materials Informatics<\/strong>, Dimitra-Danai Varsou, Andreas Tsoumanis, Panagiotis D. Kolokathis, Dimitris G. Mintis, Konstantinos D. Papavasileiou, Nikolaos, K. Sidiropoulos, Georgia Melagraki and Antreas Afantitis<\/li>\n\n\n\n
  12. Use case of Symbolic Regression in Moir\u00e9 patterns using simulated data<\/strong>, Efi-Maria Papia, Alex Kondi and Vassilios Constantoudis<\/li>\n\n\n\n
  13. Critical Node Detection in Sparse Graphs using Hopfield Neural Networks<\/strong>, Vicky Papadopoulou-Lesta, Kyriacos Neocleous and Ioannis Michos<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"_links":{"self":[{"href":"https:\/\/ainst.scify.org\/index.php?rest_route=\/wp\/v2\/pages\/67"}],"collection":[{"href":"https:\/\/ainst.scify.org\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/ainst.scify.org\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/ainst.scify.org\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/ainst.scify.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=67"}],"version-history":[{"count":12,"href":"https:\/\/ainst.scify.org\/index.php?rest_route=\/wp\/v2\/pages\/67\/revisions"}],"predecessor-version":[{"id":366,"href":"https:\/\/ainst.scify.org\/index.php?rest_route=\/wp\/v2\/pages\/67\/revisions\/366"}],"wp:attachment":[{"href":"https:\/\/ainst.scify.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=67"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}