AI has long received a lot of public attention for the functionality that it offers in a broad range of data related problems ranging from web applications with big data to smart interfaces to search optimization.\u00a0 However, AI has also been gaining a lot of traction for its ability to solve problems in the physical domain as well.\u00a0 Following is a summary of some of the more interesting technologies that I have been following lately<\/p>\n\n\n\n
Plasma Shaping for Nuclear Fusion<\/strong><\/p>\n\n\n\n Plasma is typically considered to be a fourth state of matter, similar to gases in some ways, but containing a large quantity of charged ions and typically occurring at high temperatures.\u00a0 Control of plasma is particularly challenging and important as it relates to nuclear fusion, since temperatures are far beyond the range which convention materials can withstand.<\/p>\n\n\n\n The most common solution is to maintain isolation between the superheated plasma and the walls of containment vessels using strong magnetic fields.\u00a0 Maintaining a proper shape of plasma clouds is very important since this affects efficiency, even distribution of temperature and so forth.\u00a0 In the worst case, losing control of the cloud shape can cause damage to the reactor walls, thus requiring extended shutdown and costly repairs.\u00a0 Optimized cloud shaping has traditionally been a daunting task due to the complexities of field management, precise requirements and the time needed to change field inputs. \u00a0However, researchers for DeepMind and the Swiss Plasma Center recently announced that they had used reinforcement learning to achieve a variety of specified cloud shapes.\u00a0 This technique will be particularly promising for larger, more complex fusion reactors which will likely be required in order to effectively commercialize this technology.<\/p>\n\n\n\n https:\/\/www.sciencealert.com\/physics-breakthrough-as-ai-successfully-controls-plasma-in-nuclear-fusion-experiment<\/p>\n\n\n\n Dynamic Systems<\/strong><\/p>\n\n\n\n Dynamic systems in mechanical engineering have often been considered too complex to model.\u00a0 Analysis involving such systems typically relies upon methods of estimation which have proven sufficient for many cases, but with the advent of AI there is a growing level of interest about whether these systems could actually be fully modeled and predicted (and what new advances such a capability might unlock).<\/p>\n\n\n\n Recently the National Science Foundation approved grants to investigate such topics, particularly including turbulence in compressible fluids (which includes most gases, such as the Earth\u2019s atmosphere).\u00a0 Flow of a compressible fluid around an object may generally be thought of as \u201claminar\u201d or \u201cturbulent\u201d.\u00a0 It is relatively easy to create turbulent flow, but laminar flow may only be achieved with smooth objects of conducive shape and limited size.\u00a0 One of the main disadvantages of turbulent flow is that it creates far more drag, hence such research could have enormous implications to fields such as aviation.<\/p>\n\n\n\n https:\/\/www.washington.edu\/news\/2021\/07\/29\/uw-to-lead-new-nsf-institute-for-using-artificial-intelligence-to-understand-dynamic-systems\/<\/p>\n\n\n\n Particle and Quantum Physics<\/p>\n\n\n\n AI has also been making strong inroads in the fields of particle and quantum physics, including devising experiments (sometimes accidentally) that are beyond the comprehension of the scientists that these algorithms theoretically serve.\u00a0 One such case occurred at the University of Vienna in which researchers created a program to simulate results of a quantum physics test, then added the capability to construct tests from combinations of functional elements.\u00a0 When the algorithm found a useful tests, researchers would save it, which led to increasingly complex tests built on top of other successful tests.\u00a0 Eventually the algorithm outpaced the researchers and began making discoveries that they had not contemplated.<\/p>\n\n\n\n https:\/\/www.scientificamerican.com\/article\/ai-designs-quantum-physics-experiments-beyond-what-any-human-has-conceived\/<\/p>\n\n\n\n Conclusion<\/strong><\/p>\n\n\n\n While we often think of AI as a phenomenon which occurs only in some virtual data space, it is finding traction in a range of arenas which may unlock new understandings about the physical world around us.\u00a0 Given the newness of this field and the rapid acceleration of such techniques, I expect to see some fascinating discoveries and transformative developments built upon them.<\/p>\n","protected":false},"excerpt":{"rendered":" AI has long received a lot of public attention for the functionality that it offers in a broad range of data related problems ranging from web applications with big data to smart interfaces to search optimization.\u00a0 However, AI has also been gaining a lot of traction for its ability to solve problems in the physical 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