[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126847-en":3,"doc-seo-126847-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126847,1099523882367,"Hazel","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Generative adversarial networks for data-scarce radiative heat transfer applications","Generative adversarial networks (GANs) provide robust generative capabilities, and this work applies them to synthetic spectral data generation for data-scarce radiative heat transfer, where such use had not been reported. The study demonstrates the method on a near-field radiative heat transfer example using a multilayered hyperbolic metamaterial. Effective spectral generation requires two GAN changes: Wasserstein GANs to mitigate mode collapse and conditioning to recover accurate labels. A feed-forward network augmented with CWGAN-generated data greatly improves performance under limited-data conditions. CWGANs also serve as surrogate models with better low-data accuracy than plain FFNNs. Overall, the results emphasize generative machine learning potential beyond image generation and optimization.","Mach. Learn.: Sci. Technol. 5 (2024) 015060 [https://doi.org/10.1088/2632-2153/ad33e1](https://doi.org/10.1088/2632-2153/ad33e1)  \nOPEN ACCESS  \nRECEIVED  \n13 July 2023  \nREVISED  \n16 February 2024  \nACCEPTED FOR PUBLICATION 14 March 2024  \nPUBLISHED  \n3 April 2024  \nOriginal Content from this work may be used under the terms of the  \nCreative Commons Attribution 4 .0 licence.  \nAny further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.  \nPAPER  \nGenerative adversarial networks for data-scarce radiative heat transfer applications  \nJ J García-Esteban􀁂, J C Cuevas􀁂 and J Bravo-Abad∗􀁂  \nDepartamento de Física Teórica de la Materia Condensada and Condensed Matter Physics Center (IFIMAC), Universidad Autónoma de Madrid, E-28049 Madrid, Spain  \n∗ Author to whom any correspondence should be addressed.  \n[E-mail:](E-mail: jorge.bravo@uam.es)[ jorge.bravo@uam.es](E-mail: jorge.bravo@uam.es)  \nKeywords: generative adversarial networks, near-field radiative heat transfer, spectral applications, data scarcity  \nAbstract  \nGenerative adversarial networks (GANs) are one of the most robust and versatile techniques in the field of generative artificial intelligence. In this work, we report on an application of GANs in the domain of synthetic spectral data generation for data-scarce radiative heat transfer applications, an area where their use has not been previously reported. We demonstrate the proposed approach by applying it to an illustrative problem within the realm of near-field radiative heat transfer involving a multilayered hyperbolic metamaterial. We find that a successful generation of spectral data requires two modifications to conventional GANs: (i) the introduction of Wasserstein GANs (WGANs) to avoid mode collapse, and,(ii) the conditioning ofWGANs to obtain accurate labels for the generated data. We show that a simple feed-forward neural network (FFNN), when augmented with data generated by a CWGAN, enhances significantly its performance under conditions of limited data availability. In addition, we show that CWGANs can act as a surrogate model with improved performance in the low-data regime with respect to simple FFNNs. Overall, this work contributes to highlight the potential of generative machine learning algorithms in scientific applications beyond image generation and optimization.  \n1. Introduction  \nMachine learning, a rapidly expanding area within computer science, focuses on advancing the foundationsand technology that enables machines to learn from data [1–4] . Deep learning, on the other hand, is a subset of machine learning techniques that uses artificial neural networks (ANNs) to model and solve complex data-driven problems, such as image and speech recognition [5, 6], natural language processing [7], and autonomous driving [8], among many others. With the current explosion of data and the rapid development of improved hardware and algorithms, machine learning and deep learning are becoming crucial tools in many industries, including healthcare, finance, and manufacturing.  \nMotivated by this success, machine learning and deep learning techniques are attracting increasing attention from a variety of scientific disciplines beyond computer science, revolutionizing traditional approaches to the modeling and analysis of data-driven scientific problems. In physics, these techniques are employed to tackle complex problems [9], including the representation of quantum many-body wavefunctions [10], the discovery and identification of phase transitions in condensed-matter systems [11–13], the solution of statistical problems [14], the development of novel quantum information technologies [15], the modeling of gravitational waves [16], and the design of nanophotonic devices with novel or improved functionalities [17–20]. Machine learning algorithms have been effectively used for the accelerated discovery and design of new materials and molecules in t","cbCaihY1OjWJ4qEx","https://ap.wps.com/l/cbCaihY1OjWJ4qEx","pdf",1590312,1,13,"English","en",105,"# Introduction\n## Motivation for machine learning and deep learning in science\n## GANs as a generative modeling framework","[{\"question\":\"What problem does this work address with GANs?\",\"answer\":\"It addresses synthetic spectral data generation for radiative heat transfer scenarios where available data are scarce.\"},{\"question\":\"What two modifications to conventional GANs are found to be necessary?\",\"answer\":\"The approach introduces Wasserstein GANs to avoid mode collapse and conditions the WGANs to obtain accurate labels for generated data.\"},{\"question\":\"How does the proposed method improve learning under limited data?\",\"answer\":\"A feed-forward neural network enhanced with CWGAN-generated data shows a significant performance gain in low-data availability, and CWGANs can act as surrogate models with improved low-data accuracy.\"}]","Generative adversarial networks for data-scarce radiative heat transfer applications | 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problem does this work address with GANs?","Question",{"text":75,"@type":76},"It addresses synthetic spectral data generation for radiative heat transfer scenarios where available data are scarce.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What two modifications to conventional GANs are found to be necessary?",{"text":80,"@type":76},"The approach introduces Wasserstein GANs to avoid mode collapse and conditions the WGANs to obtain accurate labels for generated data.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed method improve learning under limited data?",{"text":84,"@type":76},"A feed-forward neural network enhanced with CWGAN-generated data shows a significant performance gain in low-data availability, and CWGANs can act as surrogate models with improved low-data 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