PINN-DT: Optimizing Energy Consumption in Smart Building Using Hybrid Physics-Informed Neural Networks and Digital Twin Framework with Blockchain Security
Department of Civil Engineering, University of Texas at Arlington（德克萨斯大学阿灵顿分校土木工程系）；Department of Information Systems, Statistics and Management Science, The University of Alabama（阿拉巴马大学信息系统、统计与管理科学系）；College of Arts and Letters, School of Art, Florida Atlantic University（佛罗里达国际大学艺术与文学学院、艺术学院）；Department of Environment and Energy, Science and Research Branch, Islamic Azad University（伊斯兰阿扎德大学环境与能源系、科学与研究分支）；Department of Quantity Surveying, University of Malaya（马来大学工程量系）；Department of Computer Engineering, Islamic Azad University of Hamadan（伊斯兰阿扎德大学哈马丹分校计算机工程系）；Department of Civil, Construction, and Environmental Engineering, University of Alabama（阿拉巴马大学土木、建筑与环境工程系）
Hajar Kazemi Naeini, Roya Shomali, Abolhassan Pishahang, Hamidreza Hasanzadeh, Mahdieh Mohammadi, Saeed Asadi, Abbas Varmaghani, Ahmad Gholizadeh Lonbar
The advancement of smart grid technologies necessitates the integration of cutting-edge computational methods to enhance predictive energy optimization. This study proposes a multi-faceted approach by incorporating (1) Deep Reinforcement Learning (DRL) agents trained using data from Digital Twins (DTs) to optimize energy consumption in real time, (2) Physics-Informed Neural Networks (PINNs) to seamlessly embed physical laws within the optimization process, ensuring model accuracy and interpretability, and (3) Blockchain (BC) technology to facilitate secure and transparent communication across the smart grid infrastructure. The model was trained and validated using comprehensive datasets, including smart meter energy consumption data, renewable energy outputs, dynamic pricing, and user preferences collected from IoT devices. The proposed framework achieved superior predictive performance with a Mean Absolute Error (MAE) of 0.237 kWh, Root Mean Square Error (RMSE) of 0.298 kWh, and an R-squared (R2) value of 0.978, indicating a 97.8% explanation of data variance. Classification metrics further demonstrated the model's robustness, achieving 97.7% accuracy, 97.8% precision, 97.6% recall, and an F1 Score of 97.7%. Comparative analysis with traditional models like Linear Regression, Random Forest, SVM, LSTM, and XGBoost revealed the superior accuracy and real-time adaptability of the proposed method. In addition to enhancing energy efficiency, the model reduced energy costs by 35%, maintained a 96% user comfort index, and increased renewable energy utilization to 40%. This study demonstrates the transformative potential of integrating PINNs, DT, and Blockchain technologies to optimize energy consumption in smart grids, paving the way for sustainable, secure, and efficient energy management systems.
Data-Driven Physics-Informed Neural Networks: A Digital Twin Perspective
Cho Chun Shik Graduate School of Mobility（赵春植移动研究生院）；Korea Advanced Institute of Science and Technology（韩国科学技术院）；Seoul National University（首尔大学）；Institute of Advanced Machines and Design（先进机械与设计研究所）；College of Information Sciences and Technology（信息科学与技术学院）；The Pennsylvania State University（宾夕法尼亚州立大学）；German Aerospace Center (DLR, Deutsches Zentrum für Luft- und Raumfahrt)（德国航空航天中心）
We demonstrate a Physics-informed Neural Network (PINN) based model for real-time health monitoring of a heat exchanger, that plays a critical role in improving energy efficiency of thermal power plants. A hypernetwork based approach is used to enable the domain-decomposed PINN learn the thermal behavior of the heat exchanger in response to dynamic boundary conditions, eliminating the need to re-train. As a result, we achieve orders of magnitude reduction in inference time in comparison to existing PINNs, while maintaining the accuracy on par with the physics-based simulations. This makes the …
Physics-Informed Neural Network Digital Twin for Dynamic Tray-Wise Modeling of Distillation Columns under Transient Operating Conditions
Department of Chemical Engineering, Veer Surendra Sai University of Technology（维杰·苏雷纳·赛大学化学工程系）；Department of Computer Science & Engineering, Veer Surendra Sai University of Technology（维杰·苏雷纳·赛大学计算机科学与工程系）
Limitations of Physics-Informed Neural Networks: a Study on Smart Grid Surrogation
Department of Energy and Environment（能源与环境系）；Vicomtech（维克科技）；Department of Electronics, Information and Bioengineering（电子、信息与生物工程系）；Politecnico di Milano（米兰理工学院）
Digital Twin-Driven Zero-Shot Fault Diagnosis of Axial Piston Pumps Using Fluid-Borne Noise Signals
State Key Laboratory of Mechanical System and Vibration（机械系统与振动国家重点实验室）；Shanghai Jiao Tong University（上海交通大学）；Shanghai Platform for Smart Manufacturing Co.,Ltd（上海智能制造平台有限公司）
Beijing University of Posts and Telecommunications（北京邮电大学）；University of Electronic Science and Technology of China（电子科技大学）；University of Oslo（奥斯陆大学）；Simula Metropolitan Center for Digital Engineering（Simula都市数字工程中心）
Emerging technologies such as digital twins and 6th Generation mobile networks (6G) have accelerated the realization of edge intelligence in Industrial Internet of Things (IIoT). The integration of digital twin and 6G bridges the physical system with digital space and enables robust instant wireless connectivity. With increasing concerns on data privacy, federated learning has been regarded as a promising solution for deploying distributed data processing and learning in wireless networks. However, unreliable communication channels, limited resources, and lack of trust among users, hinder the …
Solving Oscillator Ordinary Differential Equations in the Time Domain with High Performance via Soft-constrained Physics-informed Neural Network with Small Data
College of Information Technology, Shanghai Jian Qiao University（上海建桥学院信息技术学院）；School of Urban Railway Transportation, Shanghai University of Engineering Science（上海工程技术大学城市轨道交通学院）
A Coupled Physics-Informed Neural Network for Greenhouse Climate State Reconstruction and Parameter Identification under Sparse Sensor Measurements
Centro de Modelamiento Matemático (CMM), University of Chile（智利大学数学建模中心）；College of Science, King Khalid University（哈立德国王大学理学院）；Presidency University（总统大学）
Trustworthy DNN Partition for Blockchain-enabled Digital Twin in Wireless IIoT Networks
School of Electronic and Optical Engineering, Nanjing University of Science and Technology（南京理工大学电子与光学工程学院）；Department of Computing, Hong Kong Polytechnic University（香港理工大学计算机系）；Data61, CSIRO（数据61，CSIRO）；Department of Electronics Engineering, Shanghai Jiao Tong University（上海交通大学电子工程系）；National Mobile Communications Research Laboratory, Southeast University（东南大学国家移动通信研究中心）
Blockchain-based Pseudonym Management for Vehicle Twin Migrations in Vehicular Edge Metaverse
School of Automation, Guangdong University of Technology（广东工业大学自动化学院）；School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）；School of Electronic Science and Engineering, Nanjing University（南京大学电子科学与工程学院）；Department of Electrical and Computer Engineering, Auburn University（奥本大学电气与计算机工程系）
Kumoh National Institute of Technology（金乌国立理工大学）；Pusan National University（釜山国立大学）；Deakin University（迪肯大学）；University of Houston（休斯顿大学）
Along with the massive growth of the Internet from the 1990s until now, various innovative technologies have been created to bring users breathtaking experiences with more virtual interactions in cyberspace. Many virtual environments with thousands of services and applications, from social networks to virtual gaming worlds, have been developed with immersive experience and digital transformation, but most are incoherent instead of being integrated into a platform. In this context, metaverse, a term formed by combining meta and universe, has been introduced as a shared virtual world that is fue…
