AI Models Tailored for Engineering 

AI Tools designed for engineering  

 

 The AI models and tools in Monolith are developed and enhanced through collaboration with top engineering teams on hundreds of AI projects – to complement your team’s engineering expertise and accelerate critical projects

 
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Use AI to predict fuel-sloshing noise, solve intractable physics

  • Problem: Vehicle acoustics 
  • Methods tried: CFD, physics-based simulation
  • Solution: Predict noise, and reduce testing with self-learning models

 

"The Monolith team understood what it means to work with genuine engineering problems in artificial intelligence: the needed flexibility and knowledge." 
Joachim Guilie, Curing Performance Expert  at Michelin 
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Next Test Recommender 
Optimise Your Test Plan 

Using a unique ensemble of algorithms applied in hundreds of combinations to your design, Monolith's Next Test Recommender analyses your test data to find the most critical values to run next and achieve your test coverage goals in less time and fewer steps.  

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 Anomaly Detection 
Find Data Errors Faster

Use Monolith’s Anomaly Detector to automatically inspect hundreds of channels to find potential sensor or system errors quickly.  Save time by automating repetitive tasks, find errors more reliably, and avoid costly and time-consuming re-testing.

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 Time-Series Model 
Tuned for Measurement Data 

Monolith includes data transformation tools to resample and extract features from time-dependent data, such as sensor measurements.  Using the time-series model, you can make more accurate predictions of time-series measurements.   

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Blog - Al Models in Monolith_ Deep Learning-based Time Series Model
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A commissioned study conducted by Forrester Consulting on behalf of Monolith

The State of AI in Engineering  

First-ever study on AI in product development surveys US and European automotive, aerospace and industrial engineering leaders.

Other Platform Features

Product Overview
Enterprise Platform 
Built-in Engineering Tools 
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