The future is here
It is no secret that artificial intelligence and machine learning are the most discussed engineering topic of the current decade and these models’ penetration into every single aspect of our lives appears to be unstoppable.
We at Optumatics believe that such a powerful tool can be leveraged into significant productivity gains for our clients from all sorts of industries.
Machine Learning
meets Computer Aided Engineering (CAE)
Starting with the field of CAE and physics-based simulations, we have co-developed several machine learning-based tools to aid our clients in reducing computational times of their simulations as well as their overall turnaround time and time-to-market for various processes.
Examples of CAE ML tools
- Our portfolio of machine learning based tools includes:
- Automotive Thermal Management ML-based tools for component temperature prediction
- Using ML-Augmented differential equation solvers to speedup simulation runtimes for battery electrochemical models
- Image processing for autonomous driving applications
What's next
We at Optumatics realize that ML and AI are still at their infancy stage in terms of mass adoption, and the extent of its application in CAE is still a moving target. However, we are committed to exploring new uses of these technologies and offering them to our clients.
Data Science driving your Business Needs
AI/ML Powered by your data
In addition to our experience in CAE related applications, we have used data science and machine learning to help numerous customers understand their customers better and increase their operational efficiency.
Customer Churn
Understanding customers better and building prediction models to identify potential churners.
Supply Chain Operations
By predicting consumer demand and inventory, supply chain operations can be optimized.
Delivery Route Optimization
Based on the vehicle data, GPS real-time data and traffic patterns, machine learning can be used to optimize delivery routes.
Forecasting Future Operations Performance
By using historical data from a business, forecasting future operations can be achieved and decision makers can be informed beforehand.
Risk Management in Financial Applications
The financial risk in a certain project and the creditworthiness of loan prospects can be assessed using machine learning models
Business Analytics
Advanced statistical models and machine learning can be used to gain insights from a business's data and inform decision makers on future projections for the business performance.
Product Quality Assurance
By carefully studying a production process, and adding product defect detection capabilities, inefficiencies in the production line can be identified and remedied.
Predictive Maintenance Pattern Detection
Based on historical performance of a given fleet (e.g., trains, road vehicles, etc.), insights about the vehicle maintenance schedules can be gained and improvements can be applied to increase fleet longevity.
Patient Statistical Modeling
Early identification of at-risk patients can mean the difference between life and death. Predictive models can be used to inform doctors of the expected risk levels of their patients.
Image Processing Applications
The applications of image processing in the past decade have significantly increased. They include autonomous driving applications, product visual inspection, facial recognition, text digitization, etc.