Noesis Optimus 2019.1 SP1 Win/Linux CAE流程集成和設計優化軟件 OPTIMUS是比利時NoesisSolutions公司的著名集成優化產品。NoesisSolutions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++、VisualBasic、Fortran、Java、以及其他編程語言)的程序代碼。 NoesisOptimus2019.1SP1|  NoesisSolutions,thedeveloperofOptimusandid8,announcesthereleaseofOptimusRev.2019.1.ThisreleaseenrichesOptimus’rangeofmodelingmethodspoweredbyMachineLearning,introducingstate-of-the-artEnsembleModelingandDeepNeuralNetworkModeling. AlongwithsignificantupdatestoseveralinterfaceswithleadingCAD/CAEsolutionsandamorefine-grainedcontrolonengineeringworkflowexecution,thisnew2019.1releasebringsOptimus’market-leadingPIDOtechnologytoagrowingcommunityofbothexpertandnon-expertusers. DeepNeuralNetworkModelingforhigh-dimensionalengineeringproblems BuildingabridgebetweentheaccuracyofInterpolationmodelingandthecomputationalspeedofApproximationmodeling,Optimus2019.1DeepNeuralNetworkmodelingisaperfectfitforhigh-dimensionalengineeringproblemsinvolvinglargeandnoisydatasets. OptimusDeepNeuralNetworks’capabilitytoreproducethebehaviorofcomplex,non-linearsystemswithalmostarbitraryaccuracyenablesawiderangeofapplications.Theseinclude,forexample,amuchmoreefficientintegrationofcomputationallyexpensivecomponentmodelsintosystemsimulationmodelsbyreplacingthesecomponentmodelswithhigh-fidelityFunctionalMock-upUnits(FMUs).OtherpotentialapplicationsincludetheanalysisofcomplexCFDimagestolocatespecificfeaturessuchasturbulenceortheevaluationofahighnumberofdifferentdesignswhilediscriminatingbetweenfeasibleandinfeasibledesigns. Assistingnon-expertusersthroughEnsembleModeling TheOptimus2019.1EnsembleModelingcapabilityishighlyrecommendedforengineeringproblemsthatinvolverelativelysmallandheterogeneousdatasets,andrequirefurtherengineeringexpertisetobebuiltup. EventhoughEnsembleModelingbelongstothesamemodelfamilyastheBestModelapproachintroducedwithOptimus2018.1,bothmodeltypesarefundamentallydifferent.WhereastheBestModeltypeselectsthebestmodelamongtheavailableOptimusmodelstofitagivendatasetbasedonusercriteria,EnsembleModelingcreatesanentirelynewmodelbyaveragingtheavailableOptimusmodels.EnsembleModelingisparticularlyusefulinassistingnon-expertuserstobetterunderstandtheirengineeringproblemsviaamodelaveragingapproach. Amorefine-grainedcontrolonengineeringworkflowexecution InadditiontothenewDeepNeuralNetwork&EnsembleModelingcapabilities,Optimus2019.1bringssignificantupdatestoseveralinterfaceswithleadingCAD/CAEsolutions–includingJMAGDesigner,PTCCreo5.0,CETOL10.2,NXCAEandLS-Dyna.Moreover,Optimususersnowhavemorecontrolonrejectionruleswhenbuildingengineeringsimulationworkflows.Rejectionrulesareusedtodeterminewhetheranengineeringsimulationexperimentshouldbeexcludedfrompost-processing,andtherelatednewcapabilitiesgrantamorefine-grainedcontrolonengineeringworkflowexecution. NoesisOptimus2019.1SP1 Product:NoesisOptimus Version:2019.1SP1build2019.04.11 SupportedArchitectures:x64 WebsiteHomePage:http://www.noesissolutions.com Language:english SystemRequirements:PC/Linux SupportedOperatingSystems:Cross-platform