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Best Practices for Data Driven Simulation Modeling

Artificial Intelligence (AI) is becoming more and more a part of the activities traditionally covered by the engineering analysis and simulation community. Recent advances in the application of AI, machine learning (deep learning) and predictive analytics, have brought these technologies to the fore in every area of industry.

This seminar hosted by the NAFEMS Americas Steering Committee brought together speakers from the end-user, consultancy, and academic industries to discuss where we are and how these technologies are being used to advance significantly the engineering analysis and simulation capabilities and approaches over the next 10 years.

Resource Abstract

The rapid growth of engineering data availability and the increasingly cheaper computational resources are elevating machine learning techniques as a safe and convenient tool used by engineers in their daily work. In particular, developments in machine learning algorithms and computational resources help boost engineering data analysis, decision making and design. However, this requires engineers to know the limitations and applicability of ML algorithms to specific engineering problems. In this presentation, we will highlight ESTECO best practices in applying ML methodologies to engineering data, with a special focus on how this can integrate and complement the knowledge coming from simulation.

Document Details

AuthorsDi Stefano. D Turchetto. M
Date 28th April 2021


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