Develop better products. Improve existing processes. Extract more experimental insights. Build more accurate predictive models. And do it all in less time and at a lower cost with Self-Validated Ensemble Modeling (SVEM).
Accelerate time-to-market by bridging the gap between machine learning and design of experiments
Self-Validating Ensemble Modeling platform: builds accurate and validated predictive models in 25% of the cost and time of using conventional techniques
SVEM enables you to build predictive models with a very high degree of accuracy quickly and cost-effectively by combining the power of machine learning and Design of Experiments.
Improve prediction accuracy
While traditional designs of experiments tend to generate 30 or fewer observations, machine learning generally needs thousands of observations to be effective. SVEM combines the best of both of these methods, allowing you to look at more variables per run, conduct fewer trials, and build more accurate predictive models from small data sets.
Overcome limitations in experimentation
With SVEM, you can examine a higher number of experimental variables, allowing you to see the entirety of a system. As a result, you get higher order, predictive models that are more realistic, detailed, and reliable—ultimately leading to more successful experimental outcomes in fewer runs.
Extract more information with fewer resources
The power of SVEM’s bootstrapping algorithm enables you to explore more product and process predictors than using conventional DOE. This means you can generate more comprehensive information with fewer experimental trials.
I really appreciate your teams efforts with regard to the modelling. It’s a bit of a shock to see an imperfect set of data like we provided get turned into a model that can predict process outputs with that degree of confidence.
CMC Biotechnology Company
Curious to learn more about how SVEM elevates predictive modeling for engineers and scientists?
Get answers to frequently answered questions (FAQs) on how SVEM leverages modern machine learning to enable you to fit flexible models even with small data sets for fast and reliable results.
Stability reinforcement in model-building algorithms
Extremely flexible model building
Validation for small data sets
The pace of change in today’s world is unprecedented. Engineers, scientists and researchers need SVEM to uncover hidden insights faster, reduce uncertainty, and optimize processes under tight time constraints to deliver products to market faster.
Want to learn more about how SVEM’s methodology sparks breakthroughs in various industrial applications?
Read the latest research.
Explore how SVEM’s methodology has been rigorously researched and validated to achieve flexible, robust, and reliable models for today’s complex operational processes in various industries.
SVEM for low-temp fracture energy of asphalt mixtures
Research article in the International Journal of Pavement Engineering
SVEM for optimizing a glycosylation analytic method in biologics
Research article published in BioProcessing Journal
SVEM for designs of experiments in chemical processing
Research article published in ScienceDirect
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