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Vehicle Dynamics 

Use Monolith’s intelligent exploration artificial intelligence tools to sort and visualise track test data, inform your engineers, and train machine learning models on manoeuvres to predict the forces on the vehicle during other manoeuvres that were not performed—saving testing time on complex tasks.

Smart Meters

Monolith's AI software instantly determines what smart meter designs give the best performance, while self-learning models learn from new data generated along the way, indicating which designs are most promising to investigate next through machine learning.

Wind Tunnel Testing

Build 3D artificial intelligence (AI) models that directly predict wind tunnel performance from a CAD design – faster and more accurately than Computational Fluid Dynamics (CFD) simulation alone.

1. Automatically detect faulty sensors

To avoid downstream issues, your engineers must tediously inspect test data for errors. One failed sensor or wiring malfunction can render thousands of dollars in testing useless.

• Prepare your data for optimal AI performance
• Catch bad data now so you avoid re-engineering later
• Find outliers in your test data fast with intuitive visualisation
  

2. Predict the critical tests to run

Test too much and you waste time confirming what you already know. Test too little and risk missing performance issues. Schedule, quality and your career depend on finding the balance. 

• Run the most important tests and skip the rest
• Optimize resources spent on costly test rigs and facilities
• Validate your designs faster with fewer prototype iterations 
  

3. Determine the cause of system failure

Product design issues during validation risk launch delays and lost market share. Pressure on engineers is high to identify critical parameters causing failure, quickly analyze the root cause, and predict how the product will perform in changing conditions.

• Predict what design changes will most likely fix the failure
• Identify components causing sub-optimal performance
• Avoid long delays and uncertainty in the validation process

4. Calibrate for thousands of conditions

Designing highly complex, non-linear systems that must meet stringent performance standards is challenging. Predicting which combination of inputs will deliver the optimal output, in all operating conditions, is next to impossible.

• Calibrate complex dynamic systems
• Ensure your system performs to spec in all conditions
• Find best-fit values across 1000's of inputs and conditions