Having the customers at the centre of innovation has driven successful business and product roadmaps at Noesis Solutions for many years. In the last two decades of pioneering innovation, we have an in-depth understanding of the integration and automation of engineering workflows and its role to obtain conceptual knowledge in the early stages of the design process. But this understanding also led us to grasp the fact that evolving alongside industry trends is no longer a choice to thrive in the ever-changing landscape of digital engineering.
Read MoreWith virtually limitless computational resources, organizations can execute complex simulation analyses at an unparalleled speed, enabling rapid iterations and significantly reducing time-to-insight - an interaction with David Franke, CEO, Noesis Solutions.
Read MoreIn our previous blog post, we’ve discussed Cluster Analysis in detail. Cluster Analysis (or clustering) is a so-called unsupervised machine learning approach available to Optimus users to help them structure their engineering data sets. In this post, we’re digging deeper into another unsupervised machine learning technique available in Optimus, the Self-Organizing Maps (SOM).
Read MoreEngineers regularly get buried under massive amounts of data generated through simulation and physical testing. As a result they spend a lot of time and effort to access and identify the data that matter most – assuming they have sufficient time to exploit all the data and turn these into decision metrics.
Read MoreMeeting up with leading manufacturers at last year’s Optimus World Conference provided great opportunity to learn about their latest process integration and design optimization (PIDO) achievements. They clearly expressed that more Optimus PIDO technology advancements will play a critical role in meeting the development challenges they see ahead of them.
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