A new comprehensive review introduces a pioneering exergy-based loss function for Physics-Informed Neural Network-Digital Twins (PINN-DT), promising unprecedented accuracy and real-time optimization for industrial thermal energy systems.
Thermal energy systems (TES) are the essential backbone of modern industry, playing a critical role in everything from power plants to advanced manufacturing. However, accurately predicting their performance under complex, real-world conditions has long been a challenge. Traditional simulation methods rely heavily on empirically derived formulas, which often suffer from limited predictive accuracy, narrow operational ranges, and reduced flexibility when handling geometrically complex configurations.
A groundbreaking review article published in ENGINEERING Energy systematically examines a powerful solution to these industrial bottlenecks: Physics-Informed Neural Network-Digital Twin (PINN-DT) technology. Conducted by researchers Sadegh Ataee and Mehran Ameri from Shahid Bahonar University of Kerman , the study provides a comprehensive taxonomy for applying PINN-DTs to industrial thermal challenges.
To highlight the transformative potential of this technology, the researchers detailed several core advancements:
"The development of robust physics-informed machine learning frameworks fundamentally depends on embedding appropriate physical principles through carefully designed constraint terms," the authors state.
For industries seeking to minimize energy consumption while maximizing output, this systematic review serves as a definitive roadmap for implementing digital-physical synchronization in the Industry 4.0 era.
Journal: ENGINEERING Energy
Read the full article for free: https://rdcu.be/frBqf
Cite this article: Ataee, S., Ameri, M. Physics-informed neural network-based digital twins for thermal energy systems: A review of solvability and loss function design. ENG. Energy 20, 10491 (2026). https://doi.org/10.1007/s11708-026-1049-1
ENGINEERING Energy
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Physics-informed neural network-based digital twins for thermal energy systems: A review of solvability and loss function design
10-Jun-2026