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Noesis Optimus 2019.1 SP1 Win/Linux CAE流程集成和設計優化軟件
OPTIMUS是比利時Noesis Solutions公司的著名集成優化產品。 Noesis Solutions公司作為專業的CAE流程集成和設計優化的公司具有10年以上的CAE和優化的工程經驗和深厚的技術積累,使其不僅成為軟件產品的供應商,也為用戶解決其多學科集成和優化設計問題提供寶貴的專業知識和經驗。公司多年來對新方法、新技術持之以恆的投入和開發,使得OPTIMUS多年來始終在同類產品中處於領先位置,受到用戶的接受和肯定,目前在汽車、航空航天、船舶、電子、新能源、機械、重工、醫療和電器等多個行業廣泛應用。
作為多學科的仿真集成平台,OPTIMUS能夠集成並自動化用戶的多學科仿真分析流程,實現設計-修改-再分析自動化,能應用現代設計方法(包括試驗設計、敏感度分析、響應面建模、參數優化、參數識別、可靠性設計、魯棒性設計)實現綜合優化和自動化分析。軟件涉及的學科包括幾何造型、結構分析、計算流體力學、控制、動力學、衝擊碰撞、震動噪聲和疲勞等領域。要求能夠集成這些學科所涉及到的CAD/CAE商用軟件、以及用戶自開發的(基於C/C++、Visual Basic、Fortran、Java、以及其他編程語言)的程序代碼。

Noesis Optimus 2019.1 SP1 | 
Noesis Solutions, the developer of Optimus and id8, announces the release of Optimus Rev. 2019.1. This release enriches Optimus’ range of modeling methods powered by Machine Learning, introducing state-of-the-art Ensemble Modeling and Deep Neural Network Modeling.

Along with significant updates to several interfaces with leading CAD/CAE solutions and a more fine-grained control on engineering workflow execution, this new 2019.1 release brings Optimus’ market-leading PIDO technology to a growing community of both expert and non-expert users.

Deep Neural Network Modeling for high-dimensional engineering problems

Building a bridge between the accuracy of Interpolation modeling and the computational speed of Approximation modeling, Optimus 2019.1 Deep Neural Network modeling is a perfect fit for high-dimensional engineering problems involving large and noisy data sets.

Optimus Deep Neural Networks’ capability to reproduce the behavior of complex, non-linear systems with almost arbitrary accuracy enables a wide range of applications. These include, for example, a much more efficient integration of computationally expensive component models into system simulation models by replacing these component models with high-fidelity Functional Mock-up Units (FMUs). Other potential applications include the analysis of complex CFD images to locate specific features such as turbulence or the evaluation of a high number of different designs while discriminating between feasible and infeasible designs.

Assisting non-expert users through Ensemble Modeling

The Optimus 2019.1 Ensemble Modeling capability is highly recommended for engineering problems that involve relatively small and heterogeneous data sets, and require further engineering expertise to be built up.

Even though Ensemble Modeling belongs to the same model family as the Best Model approach introduced with Optimus 2018.1, both model types are fundamentally different. Whereas the Best Model type selects the best model among the available Optimus models to fit a given data set based on user criteria, Ensemble Modeling creates an entirely new model by averaging the available Optimus models. Ensemble Modeling is particularly useful in assisting non-expert users to better understand their engineering problems via a model averaging approach.

A more fine-grained control on engineering workflow execution

In addition to the new Deep Neural Network & Ensemble Modeling capabilities, Optimus 2019.1 brings significant updates to several interfaces with leading CAD/CAE solutions – including JMAG Designer, PTC Creo 5.0, CETOL 10.2, NX CAE and LS-Dyna. Moreover, Optimus users now have more control on rejection rules when building engineering simulation workflows. Rejection rules are used to determine whether an engineering simulation experiment should be excluded from post-processing, and the related new capabilities grant a more fine-grained control on engineering workflow execution.

Noesis Optimus 2019.1 SP1
Product: Noesis Optimus
Version: 2019.1 SP1 build 2019.04.11
Supported Architectures: x64
Website Home Page :
Language: english
System Requirements: PC / Linux
Supported Operating Systems: Cross-platform