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One Number for the Question Everyone Asks: Did It Survive?
The Survivability Index™ is K-Extreme's core output: the predicted probability that a polymer, rubber, elastomer, composite, coating, adhesive, or encapsulant remains within its required performance threshold after an extreme event. It is reported with a confidence interval so teams can make return-to-service decisions with quantified risk.

87%
Survivability Probability
±4%
Confidence Interval
Probability of survivability post-event

How the Survivability Index™ Is Calculated
• The material's pre-event damage state
• The severity of the extreme event
• The performance threshold required for continued service
Output
Output is deterministic—not a conservative safety factor, not an engineering judgment, but a machine-learned physics prediction with statistical bounds.
ElastoSure Products Powering K-EXTREME
Extreme Events K-Extreme Can Model
K-Extreme handles any environment where a polymer, rubber, or composite component faces an acute or severe loading event that pushes it beyond standard operating conditions.
Real-World Proof Behind the Polymer Aging & Durability Simulation Software
Prediction Accuracy
95%
For long-term durability prediction
Experimental Tests
80,000+
Used for training and validation
vs. 6 Months
35 Days
Replacing long-weathering tests
Faster Analysis
5–10×
Compared to traditional FE models
Industries We Serve
Every industy faces unique, constantly evolving challenges. ElastoSure delivers the expertise, capabilities and tools to transform the design and production processes of industries.

K-EXTREME
Machine-learned survivability estimation.
Summary: K-Extreme is a machine-learned engine that provides conservative estimates of survivability by calculating the minimum damage capacity needed to survive single-event effects (SEE) such as: loss-of-coolant accidents in nuclear reactors, solar flares for space applications and nuclear detonation simulations for strategic defense applications. K-Extreme then takes into account the available data on different SEE and uses them to predict survivability in case of a new SEE.
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