Synthesis of Computational Mechanics and Machine Learning

Dear Colleagues,

Recognizing the potential of using data generated in engineering applications, e.g., from laboratory testing or during the monitoring of structures or engineering processes, traditional methods of computational mechanics have recently expanded towards an integration of data, leading to hybrid formats of data- and model-based prognosis models. Different procedures, ranging from methods of data-driven mechanics to enrich physics-based models by synthetic data to support of the training of machine-learning algorithms using methods of computational simulation, are currently being investigated. Exploiting the complementary advantages of physics-based modelling and data-driven learning opens new perspectives on the generation of digital twins as the backbone of the steering of engineering processes.

This Special Issue welcomes contributions from both researchers and practitioners describing recent advancements in the combination of methods of computational mechanics with methods of machine-learning targeted towards applications in engineering.

Dr. Miguel A. Bessa
Prof. Dr. Günther Meschke
Dr. Jithender J. Timothy
Guest Editors

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