The article discusses the use of digital twins in scientific research, with a focus on NASA's James Webb Space Telescope (JWST). Engineers at Raytheon, the company responsible for JWST's software and flight operations, created a digital twin of the telescope to monitor its complex deployment in space and to help troubleshoot any problems that might arise. The digital twin updates itself daily with 800 million data points and is used to train operators, predict the effects of software updates, and troubleshoot issues. The concept of digital twins was first introduced by Michael Grieves in 2002, and the term was popularized by NASA employee John Vickers in 2010. As technology has advanced, digital twins have become more common in both the defense and scientific industries, with the space industry being a particular area where the two sectors converge. The JWST's digital twin is just one example of how these twins are helping scientists run the world's most complex instruments and revealing more about the world and the universe beyond.
The paper proposes the "law of increasing functional information," a new law of nature that could help explain the evolution of complex systems across multiple scales in the universe, from atoms and molecules to stars and brains.
These systems are characterized by three attributes: they form from numerous components, processes generate numerous configurations, and configurations are preferentially selected based on function.
The law suggests that functional information of a system will increase over time when subjected to selection for function(s). The authors argue this law could help predict the behavior of these systems and provide a unified framework for understanding their evolution.
They suggest it could be a missing piece in our understanding of the universe.
These systems are characterized by three attributes: they form from numerous components, processes generate numerous configurations, and configurations are preferentially selected based on function.
The law suggests that functional information of a system will increase over time when subjected to selection for function(s). The authors argue this law could help predict the behavior of these systems and provide a unified framework for understanding their evolution.
They suggest it could be a missing piece in our understanding of the universe.
The relationship between predictability and reconstructability, and how it can vary in opposite directions in complex systems. The work is based on information theory and was performed on various dynamics on random graphs, including continuous deterministic systems, and provides analytical calculations of the uncertainty coefficients for many different systems.
