Machine Learning and Reduced Order Modeling for Large-Scale Industrial Digital Twins

Akselos’ Structural Performance Management solution

Explore the foundational technology behind Akselos' Structural Performance Management solution, Reduced Basis Finite Element Analysis (RB-FEA), with David Knezevic, the Chief Scientific Officer at Akselos.

In the dynamic world of industrial technology, the concept of digital twins is revolutionizing how companies manage and optimize their operations. Digital twins create precise digital replicas of physical assets, updated in real-time and capable of predicting the outcomes of different scenarios. This not only enhances operational efficiency but also extends asset lifespan and improves overall safety. Akselos is at the forefront of this innovation, integrating machine learning and reduced order modeling to develop sophisticated digital twins for large-scale applications.

In a recent presentation at the 'The Industrialization of SciML' workshop at Brown University, David discussed RB-FEA, comparing and contrasting it with various scientific machine learning methods in relation to large-scale industrial assets and infrastructure.

For those interested in a deeper dive into Akselos’ technology and its applications, the full presentation is available below. This resource offers a comprehensive look at the technical nuances and real-world applications of RB-FEA in digital twins, providing valuable insights into its potential to transform industrial operations. Whether you're a professional in the field or simply curious about the next wave of technological innovation, this presentation will provide a clear understanding of how digital twins are setting new standards in industrial efficiency.