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The purpose of this paper is to develop surrogate models, using deep learning (DL), that can facilitate the application of EM analysis software. In the current status quo, electrical systems can be found in an ever-increasing range of products that are part of everyone’s daily live. With the advances in technology, industries such as the automotive, communications and medical devices have been disrupted with new electrical and electronic systems. The innovation and development of such systems with increasing complexity over time has been supported by the increased use of electromagnetic (EM) analysis software. Such software enables engineers to virtually design, analyze and optimize EM systems without the need for building physical prototypes, thus helping to shorten the development cycles and consequently cut costs.

The industry standard for simulating EM problems is using either the finite difference method or the finite element method (FEM). Optimization of the design process using such methods requires significant computational resources and time. With the emergence of artificial intelligence, along with specialized tools for automatic differentiation, the use of DL has become computationally much more efficient and cheaper. These advances in machine learning have ushered in a new era in EM simulations where engineers can compute results much faster while maintaining a certain level of accuracy.

This paper proposed two different models that can compute the magnetic field distribution in EM systems. The first model is based on a recurrent neural network, which is trained through a data-driven supervised learning method. The second model is an extension to the first with the incorporation of additional physics-based information to the authors’ model. Such a DL model, which is constrained by the laws of physics, is known as a physics-informed neural network. The solutions when compared with the ground truth, computed using FEM, show promising accuracy for the authors’ DL models while reducing the computation time and resources required, as compared to previous implementations in the literature.

The paper proposes a neural network architecture and is trained with two different learning methodologies, namely, supervised and physics-based. The working of the network along with the different learning methodologies is validated over several EM problems with varying levels of complexity. Furthermore, a comparative study is performed regarding performance accuracy and computational cost to establish the efficacy of different architectures and learning methodologies.

Since the start of the Carers’ Movement research into unpaid care and carers has been used to advocate for policy change. The purpose of this paper is to address the changes in research into unpaid care and carers since the start of the Carers’ Movement and to explore the relationship between these changes and social policy.

This research paper is based on a qualitative study of documents within the Carers UK archive.

Research into unpaid care and carers has changed focus from caregiving as an identity and lifestyle to an interruption to “normal” life and employment. Changes in research are intertwined with changes in policy, with research evidencing advocation for policy change and policy change fuelling further research. Changes in the methodology of this research exposes transition points in the Carers’ Movement and in social research more broadly.

This paper contributes to critical understandings of the relationship between research into unpaid care and caring and policy. The paper also contributes to debates on methodology, exploring how the methodological zeitgeist presents in archived research.

Understanding how current research into unpaid care and carers has been developed and acknowledging the role of policy in research development brings available data on unpaid care and caring under scrutiny.

This paper is original in developing a critical analysis of the relationship between research into unpaid care and carers and social policy.