Founded in 1912, Technion-Israel Institute of Technology is Israel’s first university and its largest center of applied research. Technion is ranked among the leading technological universities worldwide.
A major source of the innovation and brainpower that drives the Israeli economy, Technion is the engine behind Israel’s renown as the iconic “Startup Nation.” Technion people, ideas, and inventions have made, and continue to make tremendous scientific contributions in fields such as medicine, sustainable energy, computer science, water conservation, and nanotechnology.
The University is proud of its four Nobel laureates – Aaron Ciechanover, Avram Hershko, Dan Shechtman, and Arieh Warshel. Technion currently ties with MIT in 8th place for the number of Nobel prize winners this century. Technion offers degrees in all fields of science and engineering, architecture and town planning, medicine, industrial management, education, and environmental studies. It houses 18 faculties, 60 research centers and institutes, and 10 interdisciplinary research frameworks. There are 14,500 students (10,000 undergraduate and 4,500 graduate) and 550 faculty members.
Since Albert Einstein founded the first Technion Society in Germany in 1923, Technion's worldwide network of friends has expanded worldwide to encompass 21 countries. In 2011, Technion and Cornell University partnered to establish an applied science and engineering institution in New York City: The Joan and Irwin Jacobs Technion Cornell Institute (JTCI). In 2013, Technion joined with Shantou University to establish the Guangdong Technion-Israel Institute of Technology (GTIT) in China.
Role In Project
The partner expertise on data analysis and machine learning will be exploited in the project for the developing of smart components of the digital twin. Sensor data that is collected over many years, as well as operational data collected over the lifetime of the simulator and its components can be used to develop predictors and models. In turn, predictors and models can accelerate the digital twin operation and can provide better analysis results. Perhaps most importantly, using data for modeling may provide the means to build simulation components when underlying rules for simulation are not available or not yet fully understood, hence must be replaced with models extracted from data.