AI-powered Design
人工智能(AI)和机器学习(ML)研究进展, combined with the increased availability of robust simulation, testing, 现场数据集使工程数据科学成为现代产品开发生命周期的关键组成部分. 由人工智能增强的计算机辅助工程(CAE)正在为制造商提供发现机器学习指导的洞察力的能力, 通过物理和人工智能驱动的工作流程，探索复杂设计问题的新解决方案, 并通过协作和设计融合实现更大的产品创新.
Design Generation
增强当前的产品开发实践，并利用AI技术成倍提高工程团队的生产力，以探索更广泛的客户满意度, high performing, and manufacturable new product design alternatives.
通过应用相同的基于物理的工具来验证从概念到设计, and through to sign-off and guided by ML using organizational specific constraints, Altair® DesignAI™ 通过在开发周期的早期自信地拒绝低潜力的设计，实现更快的设计收敛.
Design Exploration
Increase collaboration, speed up design convergence, and drive product innovation with AI-powered design tools.
对于复杂几何图形的高保真建模，分析师可以使用Altair®HyperWorks® Design Explorer an end-to-end workflow for real time performance prediction and evaluation. Automating repetitive tasks using ML, 设计资源管理器直观地执行几何图形创建和编辑的直接建模, mid-surface extraction, surface and mid-meshing, mesh quality correction, combined with efficient assembly management and process guidance.
Design Optimization
From design fine-tuning through to design synthesis, including complex multiphysics projects or the study of sets of data, Altair® HyperStudy® helps multidisciplinary teams gain insight from complex models, explore and create new concepts with a variety of inputs, determine best compromises, and support decision-making.
仿真技术与设计探索和ML相结合，使工程师能够有效地应对时间上市的挑战, 并帮助团队交付在整个开发过程中考虑更多设计维度的高性能产品.
High-fidelity Modeling Made Easy with AI
HyperWorks shapeAI makes it possible to automate pattern and shape recognition within a model, enabling the user to select all similar shapes and edit them at the same time. It uses clustering to group the parts, 允许用户建模少量的组，而不是大量的独立部分.
shapeAI包含对指定几何形状本身的自动特征提取，不需要任何额外的输入或干预. 将这些特征与HyperWorks匹配工具中的ML算法相结合，让每个用户都能轻松使用几何ML. shapeAI可以利用几何相似度对复杂模型的组件进行组织，使对一个部件的修改同步到所有部件.
Anomaly Detection and Test Rig Analytics with AI
Altair® Compose® is an environment for doing math calculations and manipulating and visualizing data, 以及对重复计算和过程自动化有用的编程和调试脚本. Compose允许用户执行各种各样的数学运算，包括信号处理.
signalAI is a library that empowers signal processing with ML. signalAI can perform data prep both in time domain and frequency domain. 然后，它可以自动训练异常检测模型来识别异常行为. In addition, for labelled data, 它可以自动训练分类模型来预测信号特征和识别测试或运行环境.
AI for Dynamic Reduced-order Model Generation
降低阶模型(ROMs)对于将详细的三维模拟整合到计算效率更高的一维环境中进行系统级研究非常有用. Simulation tools like Altair® EDEM™ or Altair CFD™ allow for detailed investigations of time-variant non-linear systems, but because of long simulation runs, analysis is typically focused on a component or subsystem. In the case of a complete system simulation however, 通常，将组件行为减少到与整个系统的交互就足够了, improving solver run time while still providing sufficiently accurate results.
Leveraging Altair’s romAI artificial intelligence tool, 3D simulations can be used as training data for generating dynamic ROMs. Only a few 3D simulation runs are required, as this approach requires less training data than traditional data-driven methods. romAI可以与任何求解器工作，并产生高度准确的结果，当操作在训练空间，甚至是稳定的和有用的外推空间. 当从测试数据开始时，同样的ML技术也可以用于系统识别目的.
Leveraging Field Data for Predictive Analytics
工程数据科学家和分析师使用牵牛星从他们的数据中产生可操作的见解. Altair® Knowledge Studio® 是市场领先的易于使用的ML和预测分析解决方案，快速可视化数据，因为它快速生成可解释的结果-不需要一行代码.
工程数据科学在广泛的产品设计和制造问题上有实际应用. 钣金冲压是汽车工业中最常见的制造工艺之一, 然而，它需要丰富的经验和人工努力来为每个部分挑选出最适当和最具成本效益的子流程.
Simulation- and Data-driven Digital Twins
Digital twins help organizations optimize product performance, gain visibility into the in-service life of a product, know when and where to perform predictive maintenance, and understand how to extend a product’s remaining useful life (RUL). 牵牛星数字双胞胎集成平台融合了物理和数据驱动的双胞胎，以支持整个产品生命周期的优化. We take a complete, open, 灵活的方法，使您的数字转型愿景在您的条件.
The physics based, simulation-driven digital twin leverages standardized, tool independent interfaces like the functional mock-up interface (FMI), co-simulation methods with geometry-based 3D CAE tools, 并采用降阶建模的方法，从详细的仿真中导出低保真度模型. 数据驱动的双胞胎使用ML算法和数据科学来优化产品性能. Looking at the problem through this lens allows you to get fast, 实时洞察产品状态，然后做出适当的操作调整，以提高产品的寿命，避免故障.
