System Calibration

Monolith’s System Calibration module is an AI-powered tool designed to help engineers calibrate complex systems faster and more accurately. By leveraging machine learning models trained on test data, it enables teams to optimize system performance with fewer tests, less data, and greater confidence. The module is ideal for scenarios where traditional simulation tools fall short due to system complexity or nonlinearity.

Features

• AI-Guided Optimization: Uses trained models to explore thousands of parameter combinations and identify optimal calibration settings.
• Interactive Candidate Review: Engineers can interactively evaluate top-performing configurations to make informed decisions.
• Test Data Integration: Builds models from existing test data to simulate system behavior under various conditions.
• Efficient Data Collection: Recommends targeted tests to reduce redundant data gathering.
• Performance Targeting: Allows users to define performance goals and constraints for calibration.

Capabilities

• Complex System Modeling: Handles nonlinear, multi-variable systems that are difficult to simulate with traditional tools.
• Trade-Off Analysis: Supports balancing multiple input parameters to meet competing performance requirements.
• Faster Decision-Making: Reduces the time needed to reach calibration decisions by automating analysis and prediction.
• Scalable Deployment: Integrates with Monolith’s Core Platform and scales across enterprise engineering teams.

Benefits

• Reduced Testing Time and Cost: Minimizes the need for extensive field or lab testing by predicting outcomes with AI.
• Improved Accuracy: Enhances calibration precision by identifying optimal parameter sets from large datasets.
• Increased Confidence: Engineers can make decisions backed by data-driven insights and predictive modeling.
• Accelerated Product Development: Speeds up the calibration phase, helping teams meet deadlines and certification requirements.