[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124350-en":3,"doc-seo-124350-105":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},124350,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","A Machine Learning Model for Humidity Estimation Based on Physics-Informed Dimensionality Reduction","Accurate and efficient humidity estimation underpins environmental monitoring, agriculture, and industrial process control, but low-cost humidity sensors often suffer from slow response dynamics, nonlinear behavior, calibration drift, and environmental sensitivity. A two-stage physics-aware machine learning framework is presented to estimate humidity from microelectrode voltage discharge dynamics. Physics-Informed Dimensionality Reduction extracts low-dimensional features governed by anomalous diffusion, while Neural Parameter Estimation learns model parameters from synthetically augmented data with limited experiments. These features, combined with temperature, feed a compact ANN for humidity prediction.","POLITECNICO DI TORINO  \nRepository ISTITUZIONALE  \nA Machine Learning Model for Humidity Estimation Based on Physics-Informed Dimensionality Reduction  \nOriginal  \nA Machine Learning Model for Humidity Estimation Based on Physics-Informed Dimensionality Reduction / Licciardi, Alessandro; Bernardi, Sara; Pizzi, Marco; Begnamino, Paolo; Rondoni, Lamberto. -In: NONLINEAR DYNAMICS. -ISSN 0924-090X. - (2025) . [10 . 1007/s11071-025-11771-3]  \nAvailability:  \nThis version is available at: 11583/3002968 since: 2025-09-12T08:23:09Z  \nPublisher:  \nSpringer Nature  \nPublished  \nDOI:10.1007/s11071-025-11771-3  \nTerms of use:  \nThis article is made available under terms and conditions as specified in the corresponding bibliographic description in the repository  \nPublisher copyright  \n(Article begins on next page)  \n04 October 2025  \nNonlinear Dyn  \n[https://doi.org/10.1007/s11071-025-11771-3](https://doi.org/10.1007/s11071-025-11771-3)  \nRESEARCH  \nA Machine Learning Model for Humidity Estimation Based on Physics-Informed Dimensionality Reduction  \nAlessandro Licciardi · Sara Bernardi · Marco Pizzi · Paolo Begnamino ·  \nLamberto Rondoni  \nReceived: 30 May 2025 / Revised: 28 August 2025 / Accepted: 31 August 2025 © The Author(s) 2025  \nAbstract Accurate and efficient humidity estimation is critical for various applications, particularly with the rise of IoT devices and smart sensors where computational resources are limited. A common issue with many conventional humidity sensors is their slow response dynamics, which restricts their performance in applications requiring rapid, real-time data. This work introduces a novel Machine Learning algorithm for fast humidity estimation, based on analyzing the voltage discharge dynamics across microelectrodes, computationally frugal yet accurate estimation method suitable for resource-constrained environments. We propose a Physics-Informed Dimensionality Reduc-  \nA. Licciardi · S. Bernardi · L. Rondoni  \nDepartment of Mathematical Sciences, Politecnico di Torino, Cso. Duca degli Abruzzi 24, 10129 Torino, Italy  \ne-mail: [sara.bernardi@cnr.it](sara.bernardi@cnr.it)  \nL. Rondoni  \ne-mail: lamberto.rondoni@polito.it  \nA. Licciardi (B) · L. Rondoni  \nINFN, Sezione di Torino, Torino 10125, Italy e-mail: alessandro.licciardi@polito.it  \nM. Pizzi · P. Begnamino  \nResearch Department, Eltek S.p.A., Strada Valenza 7, 15033 Casale Monferrato, Italy  \n[e-mail: m.pizzi@eltekgroup.it](e-mail: m.pizzi@eltekgroup.it)  \nP. Begnamino[e-mail: p.begnamino@eltekgroup.it](e-mail: p.begnamino@eltekgroup.it)  \nS. Bernardi  \nInstitute of Atmospheric Sciences and Climate, National Research Council of Italy, Corso Fiume 4, 10133 Torino, Italy  \ntion (PIDR) methodology that leverages an underlying physical model – specifically, anomalous diffusion governing the electric discharge between microelectrodes – to extract low-dimensional, physically meaningful features from high-dimensional sensor time series data. Neural Parameter Estimation (NPE), trained effectively on synthetically augmented data guided by limited experimental observations, maps the voltage discharge curves to the anomalous diffusion parameters of the physical model. These parameters, representing a low dimensional physical space, are then fed alongside temperature readings into a compact Artificial Neural Network (ANN) for final humidity prediction. This two-stage, physics-aware architecture significantly reduces model complexity. Experimental results demonstrate the effectiveness of the PIDR approach, achieving high prediction accuracy while demanding significantly less computational effort and training data than traditional parameter estimation techniques or purely data-driven deep learning models applied to raw data. Our study highlights the successful integration of physical principles with machine learn  \ning for developing efficient, interpretable, and robust AI solutions tailored for smart sensors and sustainable IoT applications.  \nKeywords Machine Learning ·","cbCaiuLrldj52G07","https://ap.wps.com/l/cbCaiuLrldj52G07","pdf",1915969,1,24,"English","en",105,"# Abstract\n# Keywords\n# Introduction\n## Motivation and challenges\n## Capacitive sensing and slow response dynamics\n## Physics-aware machine learning objective\n# Method overview\n## Voltage discharge and conductivity link\n## Physics-Informed Dimensionality Reduction (PIDR)\n## Neural Parameter Estimation (NPE)\n## Compact ANN humidity predictor","[{\"question\":\"What problem does the model address in humidity sensing?\",\"answer\":\"It targets accurate, fast humidity estimation despite slow response dynamics and limitations of conventional humidity sensors on real-time, resource-constrained platforms.\"},{\"question\":\"How does Physics-Informed Dimensionality Reduction (PIDR) work in the approach?\",\"answer\":\"PIDR uses an underlying physical model of anomalous diffusion to extract low-dimensional, physically meaningful features from high-dimensional sensor voltage time series.\"},{\"question\":\"How is humidity ultimately predicted?\",\"answer\":\"Neural Parameter Estimation maps voltage discharge curves to the physical model’s low-dimensional parameters, which together with temperature are input to a compact artificial neural network for final humidity prediction.\"}]","A Machine Learning Model for Humidity Estimation Based on Physics-Informed Dimensionality Reduction | 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problem does the model address in humidity sensing?","Question",{"text":75,"@type":76},"It targets accurate, fast humidity estimation despite slow response dynamics and limitations of conventional humidity sensors on real-time, resource-constrained platforms.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does Physics-Informed Dimensionality Reduction (PIDR) work in the approach?",{"text":80,"@type":76},"PIDR uses an underlying physical model of anomalous diffusion to extract low-dimensional, physically meaningful features from high-dimensional sensor voltage time series.",{"name":82,"@type":73,"acceptedAnswer":83},"How is humidity ultimately predicted?",{"text":84,"@type":76},"Neural Parameter Estimation maps voltage discharge curves to the physical model’s low-dimensional parameters, which together with temperature are input to a compact artificial neural network for final humidity 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