Monolith

The Monolith Platform is a cloud-based AI software solution designed specifically for engineers to solve complex physics problems in various industries. According to the vendor, it empowers engineering domain experts in automotive, aerospace, industrial, defense, pharmaceutical, energy, and manufacturing sectors to leverage artificial intelligence and machine learning models. Monolith caters to engineering teams of all sizes, from small startups to large enterprises.

Key Features

Self Learning Models: According to the vendor, engineers can utilize engineering data to create accurate self-learning models and deploy them to predict performance under various operating conditions, allowing for instant predictions of complex system performance.

No-Code AI Modelling: The vendor states that engineers can build AI models without coding or a PhD in statistics using a user-friendly, no-code interface. They can import data and correlate inputs and outputs for an end-to-end process, simplifying model building.

Model Training and Evaluation: The vendor claims that engineers can evaluate the quality of AI models using no-code tools and ensure accurate predictions by assessing model performance. They can choose the best AI model for their specific use case, enhancing the reliability of predictions.

AI Model Prediction: According to the vendor, engineers can instantly predict the performance or quality of new designs, eliminating the need for expensive physical tests. They can leverage the computer vision dashboard for easy visualization of AI predictions.

AI Model Uncertainty Quantification: The vendor states that engineers can measure prediction uncertainty to build trust in AI models and identify regions of the design space with sparse or volatile data. They can gain insights into the reliability of AI predictions, enabling informed decision-making.

AI Optimization: According to the vendor, engineers can query AI models to find the best designs and operating conditions, optimizing performance metrics and constraints using no-code tools. This can streamline the design process and accelerate decision-making.

Explainable AI: The vendor claims that engineers can rank design variables and test conditions that affect performance, focusing efforts on variables with the most impact on product performance. This can enhance interpretability and understanding of AI models for improved decision-making.

3D Deep Learning: According to the vendor, engineers can predict the performance of new 3D CAD designs without the need for data science resources. They can generate performance-optimized 3D designs using AI and predict entire 3D fields of data for comprehensive analysis.

Next Test Recommender (NTR): AI-Powered Test Plan Optimization: The vendor states that engineers can train and assess machine learning models for optimal test conditions, receive valuable recommendations for new tests based on existing data, and improve testing efficiency and effectiveness with AI recommendations.

Workflow & API Integration: According to the vendor, engineers can connect Monolith to existing data storage platforms, upload and manage raw data files, and seamlessly integrate Monolith dashboards and models into existing workflows for enhanced productivity.