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Researchers used generative AI to develop a physics-informed technique to classify phase transitions in materials or physical systems that is much more efficient than existing machine-learning approaches. The work was led by researchers at MIT and the University of Basel.
In fields such as physics and engineering, partial differential equations (PDEs) are used to model complex physical processes to generate insight into how some of the most complicated physical and natural systems in the world function.To solve these
MIT researchers propose a "physics-enhanced deep-surrogate" (PEDS) method for developing data-driven surrogate models for complex physical systems in such fields as mechanics, optics, thermal transport, fluid dynamics, physical chemistry, and climate modeling.
MIT CSAIL research affiliate Chris Rackauckas often uses his spare time to answer questions about numerical differential equations that people have posed online.
The official website for the Julia Language. Julia is a language that is fast, dynamic, easy to use, and open source. Click here to learn more.