Process Integration and Design Optimization Platform Updated

Optimus Rev. 2018.1 introduces modeling methods augmented with machine learning

Optimus Rev. 2018.1 introduces modeling methods augmented with machine learning

Noesis Solutions has released Rev. 2018.1 of its Optimus process integration and design optimization platform. The platform offers a vendor-neutral, open architecture and a range of technologies intended to help organizations deploy an objective-driven engineering simulation process. Image courtesy of Noesis Solutions NV.

Noesis Solutions, developer of the Optimus process integration and design optimization (PIDO) software platform and id8 for objectives driven engineering, has released Optimus Rev. 2018.1. This release introduces new modeling methods boosted by machine learning technologies, the company says. It reports further that many of the new updates of the Rev. 2018.1 edition enhance design space exploration and optimization capabilities as well as increase compatibility with widely deployed CAD/CAE solutions.

Optimus enables engineering teams to perform automated searches to identify the optimum design parameters that best match performance objectives and design constraints. Image courtesy of Noesis Solutions NV.

Optimus is described as a vendor-neutral, open platform that provides an open architecture to communicate with any engineering software, including the capability to deal with any file syntax. This, explains the company, enables organizations to deploy an “Objectives Driven Engineering” process deployment strategy with their preferred engineering software as well as safeguard legacy codes. Among the technologies Optimus provides are design space exploration and engineering optimization methods.

Noesis Solutions reports that the new machine learning enhanced modeling methods introduced in Optimus Rev. 2018.1 enable engineering teams to create accurate models for low- and high-dimensional optimization problems even with a limited number of training data sets. The platform's new Light Weight Neural Networks (LWNN), Random Forest Regression (RFR) and Relevance Vector Regression (RVR) algorithms are also said to help users accurately model large data sets quickly. Further, a new Best Model algorithm reportedly can help non-expert users by automatically selecting the best-performing model for any given data set among the models available in Optimus.

Optimus provides a range of technologies for design exploration, optimization and workflow automation that can help organizations increase the efficiency of development processes with their preferred tools. It offers interfaces for 40 widely deployed CAD/CAE solutions. Image courtesy of Noesis Solutions NV.

Optimus Rev. 2018.1 brings several improvements to Optimus’ Adaptive DOE (design of experiments) algorithm that learns from available data points and iteratively adds extra data samples in design space regions that matter most. This new Optimus release also extends the capabilities of NAVIRUN, the one-click optimizer Noesis Advanced adviser, with new support for discrete optimization problems.

Optimus offers users a total of 40 interfaces to leading CAD/CAE solutions. The Rev. 2018.1 edition comes with a updates for the latest versions of SimulationX, PTC Creo, Abaqus, CATIA, Microsoft Excel, FloMASTER, HDF5, RomaxDESIGNER, JMAG and MapleSim. Optimus Rev. 2018.01 also supports ANSYS Electronics Desktop on all operating systems that Optimus is compatible with.

For further details on the Optimus process integration and design optimization platform, go here.

Watch a video overview of Optimus.

Learn about how Optimus enables objectives driven engineering.

Check out how Noesis' solutions are deployed in a variety of industries.

See why DE's Editors selected Optimus Rev. 2018.1 as their Pick of the Week.

Sources: Press materials received from the company and additional information gleaned from the company's website.

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Anthony J. Lockwood's avatar
Anthony J. Lockwood

Anthony J. Lockwood is Digital Engineering’s founding editor. He is now retired. Contact him via de-editors@digitaleng.news.

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