Utilities - Arlington, Virginia, United States
Kronos Fusion Energy Algorithms have been leveraging over a 100 billion dollars in global government research endeavors plus 60 years of Fusion Research and leap-frogging similar current thought processes in accurately demonstrating the capacity of deep learning to forecast THESE disruptions — Decreasing the error rate here reduces the sudden loss of confinement of plasma particles and energy — Machine Learning algorithms in our Kronos Fusion Systems drive to lower THIS error rate - thus lowering the costs on our fusion energy generators by 17-20% compared to every other chartered to be build.Tuning deep neural networks is a computationally intensive problem that requires the engagement of high-performance computing clusters. The first few principles-based approaches hit close to 80% predictive capability. They were sometimes not better than a coin flip. ALL current timelines & financials for commercial fusion energy generators are without our solution to the industry benchmarks. Our simulations show that our first Fusion Energy Generator would be 20% cheaper to build and operate than any others set to launch for the next 40 years. OUR second and third generators would subsequently have a 10% price drop to build and increases our asset value by 40%.Our machine learning-based statistical methods support vector machines like the ones at the International Thermonuclear Experimental Reactor or ITER which could get up to 85% or better accuracy rate with less than 5% false positives. To improve upon these prediction rates, we at Kronos Fusion, trained a neural network capable of taking into account far more variables than the earlier support vector machines with hyperparameter tuning.Our software continues to demonstrate its ability to predict true disruptions within the 30-millisecond time frame that ITER will require while reducing the number of false alarms.
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