Index-value, or so-called n-value prediction is of paramount importance for understanding the superconductors’ behaviour specially when modeling of superconductors is needed. This parameter is dependent on several physical quantities including temperature, the magnetic field’s density and orientation, and affects the behaviour of high-temperature superconducting devices made out of coated conductors in terms of losses and quench propagation. In this paper, a comprehensive analysis of many machine learning (ML) methods for estimating the n-value has been carried out. The results demonstrated that cascade forward neural network (CFNN) excels in this scope. Despite needing considerably higher training time when compared to the other attempted models, it performs at the highest accuracy, with 0.48 root mean squared error (RMSE) and 99.72% Pearson coefficient for goodness of fit (R-squared). In contrast, the rigid regression method had the worst predictions with 4.92 RMSE and 37.29% R-squared. Also, random forest, boosting methods, and simple feed forward neural network can be considered as a middle accuracy model with faster training time than CFNN. The findings of this study not only advance modeling of superconductors but also pave the way for applications and further research on ML plug-and-play codes for superconducting studies including modeling of superconducting devices.

The evaluation and estimation of the electric and magnetic field (EMF) intensity in the vicinity of overhead transmission lines (OHTL) is of paramount importance for residents’ healthcare and industrial monitoring purposes. Using artificial intelligence (AI) techniques makes researchers able to estimate EMF with extremely high accuracy in a significantly short time. In this paper, two models based on the Artificial Neural Network (ANN) have been developed for estimating electric and magnetic fields, i.e., feed-forward neural network (FFNN) and cascade-forward neural network (CFNN). By performing the sensitivity analysis on controlling/hyper-parameters of these two ANN models, the best setup resulting in the highest possible accuracy considering their response time has been chosen. Overall, the CFNN achieved a significant 56% reduction in Root Mean Squared Error (RMSE) for the electric field and a 5% reduction for the magnetic field, compared to the FFNN. This indicates that the CFNN model provided more accurate predictions, particularly for the electric field than the proposed methods in other recent works, making it a promising choice for this application. When the model is trained, it will be tested by a different dataset. Then, the accuracy and response time of the model for new data points of that layout will be evaluated through this process. The model can predict the fields with an accuracy near 99.999% of the actual values in times under 10 ms. Also, the results of sensitivity analysis indicated that the CFNN models with triple and double hidden layers are the best options for the electric and magnetic field estimation, respectively.

Thermal stresses in superconducting tapes, resulting from quench, over-current conditions, or high-current faults, can rapidly elevate temperatures, potentially causing hot spots, performance degradation, localized damage, or catastrophic burnout failure if heat dissipation is inadequately managed. Traditional numerical approaches, such as finite element analysis (FEA), accurately simulate these transient thermal behaviors but suffer from significant computational burdens, complex meshing processes, and sensitivity to input and meshing parameters. Moreover, FEA results often lack generalization capability, necessitating new simulations for each variation in problem parameters. Recently, artificial intelligence (AI) and machine learning (ML) methods have emerged as alternatives for addressing superconductivity problems. However, existing AI/ML models typically operate as black boxes, providing little insight into the physics-based relationships between inputs and outputs. To address this limitation, this study introduces a physics-informed neural network (PINN) framework specifically designed to model transient thermal behaviors in superconducting tapes. By embedding governing physics laws directly into the neural network’s training process, PINNs offer increased transparency, accuracy, and interpretability compared to conventional AI/ML models. This paper, for the first time, aims to demonstrate PINNs as an innovative modeling approach for superconductors, focusing explicitly on transient thermal responses following a heat pulse in superconducting tapes. Validation against analytical solutions and benchmark FEA simulations confirms the PINN model’s effectiveness in accurately capturing sharp thermal gradients and transient heat transfer. Furthermore, results highlight the model’s excellent generalization and extrapolation capabilities, illustrating that a trained PINN can reliably predict scenarios beyond its original training dataset, thereby offering significant potential for tackling complex challenges related to superconductors.

The advent of superconducting bulks, due to their compactness and performance, offers new perspectives and opportunities in many applications and sectors, such as magnetic field shielding, motors/generators, NMR/MRI, magnetic bearings, flywheel energy storage, Maglev trains, among others. The investigation and characterization of bulks typically relies on time-consuming and expensive experimental campaigns; hence the development of effective surrogate models would considerably speed up the research progress around them. In this study, we first produced an experimental dataset containing the levitation and lateral forces between different MgB₂ bulks and one permanent magnet under different operating conditions. Next, we have exploited the dataset to develop surrogate models based on Artificial Intelligence (AI) techniques, namely Extremely Gradient Boosting, Support Vector Regressor (SVR), and Kernel Ridge Regression. After the tuning of the hyperparameters of the AI models, the results demonstrated that SVR is the superior technique and can predict levitation and lateral forces with a worst-case accuracy scenario 99.86% in terms of goodness of fit to experimental data. Moreover, the response time of these models for the estimation of new datapoints is ultra-fast.

The rising demand for advanced energy systems requires enhanced thermal management strategies to maximize resource utilization and productivity. This is quite an important industrial and academic trend as the efficiency of energy systems depends on the cooling systems. This study intends to address the critical need for efficient heat transfer mechanisms in industrial energy systems, particularly those relying on pool boiling conditions, by mainly focusing on Critical Heat Flux (CHF). In fact, CHF keeps a limit in thermal system design, beyond which the efficiency of the system drops. Recent research materials have highlighted nanofluids’ superior heat transfer properties over conventional pure fluids, like water, which makes them a considerable substitution for improving CHF in cooling systems. However, the broad variability in experimental outcomes challenges the development of a unified predictive model. Besides, Machine Learning (ML) based prediction has shown great accuracy for modeling of the designing parameters, including CHF. Utilizing ML algorithms—Cascade Forward Neural Network (CFNN), Extreme Gradient Boosting (XGBoost), Extra Tree, and Light Gradient Boosting Method (LightGBM)— four predictive models have been developed and the benchmark shows CFNN’s superior accuracy with an average goodness of fit of 89.32%, significantly higher than any available model in the literature. Also, the iterative stability analysis demonstrated that this model with a 0.0348 standard deviation and 0.0268 mean absolute deviation is the most stable and robust method that its performance minorly changes with input data. The novelty of the work mainly lies in the prediction of CHF with these advanced algorithm models to enhance the reliability and accuracy of CHF prediction for designing purposes, which are capable of considering many effective parameters into account with much higher accuracy than mathematical fittings. This study not only explains the complex interplay of nanofluid parameters affecting CHF but also offers practical implications for the design of more efficient thermal management systems, thereby contributing to the broader field of energy system enhancement through innovative cooling solutions.