AI Big Data Analysis

The process of using AI big data to build predictive models to assist analysis can be roughly divided into several steps:

1. Define the goal:
　　Set the topic and objectives based on the problem and requirements.

2. Prepare the data:
　　Data can come from simulation results, experimental measurements, production lines, or quality inspection records. If there are missing values, the data needs to be cleaned or supplemented using appropriate methods.

3. Build the AI predictive model:
　　Train the AI model using big data. After training and validation, the AI model can be used to assist prediction and analysis. In addition, the predictive model can be further deployed to a real-time monitoring platform to detect anomalies and outliers, thereby achieving real-time predictive analytics.

Further Reading:【YouTube】AI Big Data Analytics and Result Prediction

RapidMiner Machine Learning and Predictive Analytics Solution

       RapidMiner is a powerful data science and machine learning platform. Since its founding in 2001, it has introduced technologies such as automated machine learning, deep learning, and cloud deployment, and was named by Gartner in 2017 as a ‘Leader in Data Science and Machine Learning Platforms’.

• Supports importing multiple data file formats
• Visual and interactive data preparation tools
• Build predictive models
• AI Hub real-time prediction

↘ Learn more about RapidMiner

Knowledge Studio Machine Learning and Predictive Analytics Solution

       Knowledge Studio is an advanced AI big data predictive analytics solution with a visual interface that quickly turns data analysis into visual and easy-to-interpret results. During use, users do not need any programming experience; they can build predictive models and analyze data simply by dragging and dropping functional blocks.

• No-code & visual interface
• Includes automated machine learning (AutoML) functionality
• Export code for deployment
• Model export supports multiple programming languages, including Python, R, SAS, etc.

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ExpertAI(HyperWorks)

       Altair ExpertAI adds an additional “unsupervised machine learning” mechanism to Design Explorer in HyperWorks.
       Based on criteria set by the user, it guides the optimization tool to “classify / cluster (clustering analysis)” the results of design exploration, and then reuse these groupings as constraints for new optimization runs.
       Altair ExpertAI is now seamlessly integrated into HyperWorks products, becoming a powerful tool for AI-augmented simulation and generative design.

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physicsAI(HyperWorks)

       PhysicsAI is not limited by parameter-based studies and can act directly on meshes or even CAD models. Based on the latest technology in geometric deep learning (GDL), PhysicsAI can learn the relationship between geometry and any physical behavior. Using PhysicsAI models for prediction can be 10 to 100 times more efficient than solver-based computation, enabling design and development teams to evaluate more design concepts (within the same schedule) and make better design decisions.

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Monarch A Fast and Effective Solution for Large-scale Data Preparation

 

       Monarch is a solution that can quickly and effectively organize large volumes of data. Monarch can connect to multiple data sources, including structured and unstructured data, cloud-based databases, and big data. It can consolidate, filter, and organize data from different documents without writing any code, allowing you to complete data preparation quickly and accurately so more time can be spent on extracting value from data instead of wasting time on time-consuming data processing.

• Over 80 prebuilt functions

• No-code

• Helps consolidate document data from different sources

• Supports various document formats

• Including txt,

• Quickly assists with completing data preparation tasks

↘ Learn more about Monarch