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The Science Behind Karax: Physics-Informed Polymer Aging Prediction
At Karax, we combine advanced material science with predictive computing to simulate polymer aging, rubber fatigue, and composite degradation—helping engineers predict long-term performance before failure occurs.
Traditional material qualification can take months and cost millions. Karax replaces guesswork with physics-based prediction, combining advanced material models with decades of degradation data. Our ElastoSure platform enables aerospace, defense, automotive, and energy organizations to simulate years of environmental and mechanical aging in just days.
Our Founder
Prof. Roozbeh Dargazany
Dr. Dargazany provides the core technical vision and structural framework of ElastoSure. His research group's pioneering work on Reduced-PINN stiff-kinetics solvers and homogenized fiber-composite mechanics forms the algorithmic foundation for Karax's simulation tools. As Associate Professor of Civil and Environmental Engineering at Michigan State University, Dr. Dargazany leads the High-Performance Materials Group, whose published research on multi-stressor polymer aging under thermal, radiation, mechanical, and hygrothermal conditions is the scientific backbone of the ElastoSure platform.
Advisory Board
Prof. Hisaki Kudo
Polymer/Rubber Aging in Extreme Conditions
School of Engineering, The University of Tokyo
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Bridges deep chemistry and commercial FE-deployable engineering for life-time prediction of Elastomers
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Work spans cable lifetime analysis, nuclear radiation curing kinetics and inverse material characterization
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Co-author on nuclear cable insulation and elastomer aging publications
Dr. Kenneth T. Gillen
Rubber Aging & Lifetime Prediction
Sandia National Laboratories (Retired)
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One of the world's foremost authorities on polymer degradation and service-life prediction
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Developed foundational methods for combined thermal, radiation, humidity, and mechanical stress
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Inventor of the modulus profiler, led to structural understanding of Diffusion-Limited Oxidation (DLO)
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Recipient of the ACS Rubber Division Melvin Mooney Distinguished Technology Award
Dr. Omid Nabnejad
Rubber Chemistry, Composites & Polymer Materials
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Expert in polymer processing, composite mechanical characterization, thermal analysis, and materials durability
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520+ citations across 17 publications
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Leads experimental characterization and composite benchmarking collaboration
Karax LLC was selected to develop the first hybrid physics-AI driven engine, K-Fail, for digital twinning of Material Aging in space. The contract was funded through the prestigious SpaceWERX Award.
DOE - Nuclear Energy Office
Karax LLC collaborates to develop K-Load software to predict aging of Cable insulation in long-term Gamma-thermal Exposure . The contract was funded through the Department of Energy-Nuclear Research Office.
NAVAIR Navy
Karax LLC's was selected to develop the Physics-based K-Load Software to predict performance loss of Polymer composites in Naval Operations. The project was sponsored by the Naval Air Systems Command (NAVAIR).
US AirForce Manufacturing Technology
Karax LLC developed K-Extreme to optimize material radiation shielding through digital design, funded by the U.S. Air Force ManTech Office.
NAVSEA
Navy
Karax LLC collaborates to develop K-Fail software for prediction of Polymer performance in Extreme Heat and Pyrolysis. The contract was funded through Naval NAVSEA..
DOE - Nuclear Energy Office
Karax LLC developed K-Load to analyze FDR-based NDE data for cable condition monitoring and remaining life prediction, funded by the DOE Office of Nuclear Energy.
Why Technical Leaders Choose Karax?
Physics-Informed AI
Unlike black-box ML models, our software enforces the strict laws of thermodynamics and chemistry. Predictions are physically admissible — not just statistically fitted.
Enterprise Integration
Our algorithmic architecture exports directly into industrial workflows like ABAQUS, ANSYS, and FE solvers. No re-tooling of your existing pipeline.
Multi-Stressor Capabilities
We map the simultaneous, complex interplay of thermal cycling, UV/radiation, chemical exposure, and mechanical load — the real conditions your materials face.
Validated Accuracy
Every K-Suite module is anchored by peer-reviewed publications and laboratory data. 95% accuracy improvement validated in published CNPC industrial trials.
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
Publication List
Macromolecular Theory and Simulations
2026 — vol. 35(1), e00044
Aging of FEPM Material in High-Pressure High-Temperature Hydrogen Sulfide Downhole Environment: Theory, Modeling, Experiments, and Material Lifetime Prediction
Ghaderi, A.; Nouri, H.; Dargazany, R.; Meng, S.; Xing, P.; Ren, J.; Cheng, P.
DOI: Here
Composites Science and Technology
2026 — vol. 111687
Physics-constrained data-driven model for elastomers under simultaneous thermal and radiation exposure
Nasiri, P.; Fifield, L.S.; Nouri, H.; Kudo, H.; Dargazany, R.
DOI: Here
Mechanics of Materials (Elsevier)
2025 — pages 105548
A physics-informed neural network model to predict thermo-oxidative/thermal aging of viscoelastic materials
Naderi, H.; Dargazany, R.
DOI: Here
International Journal of Solids and Structures (Elsevier)
2022 — vol. 252, pages 111800
Thermal aging coupled with cyclic fatigue in cross-linked polymers: Constitutive modeling & FE implementation
Bahrololoumi, A.; Shaafaey, M.; Ayoub, G.; Dargazany, R.
DOI: Here