Invention:
This technology is an algorithm/method that optimizes the potential knowledge gained about a complex system when performing robustness testing with a set of constraints (cost, resources, etc.,). It can be used to optimize which test should be run based on the constraints and is particularly useful for prioritizing tests based on both the cost of each test and the uncertainty associated with the knowledge of the system being tested.
Background:
Optimization algorithms have diverse applications in many industries, but this algorithm is primarily for use in defense and biopharmaceuticals. It would be ideal for companies that need to test complex products with many possible modes of configuration. The related markets are expected to see mid-to-high levels of growth before 2030 with machine learning having a large influence on market growth and technology adoption.
Applications:
- Defense
- Machine learning
- Biopharmaceuticals
- Software testing services
- Decision and knowledge management software
Advantages:
- Useful for prioritizing tests based on cost and system uncertainty
- Optimizes the potential knowledge gained about a complex system
- Ideal for companies that must test complex products with many configurations
