Institute for Future Intelligence

Artificial intelligence (AI) is transforming the way people work. IFI is spearheading research on this human-technology frontier based on Aladdin, an experimental platform being developed to reimagine engineering design empowered by various types of AI — in the context of renewable energy and energy efficiency. Aladdin supports people to design their own energy future with the assistance of AI. As design is generative in essence, we envision that generative AI (GenAI) will play a central role in next generation design technologies.

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Scientists have long relied on imaging to visualize invisible phenomena and thus advance science. IFI is developing the Infrared Explorer to realize the educational promise of scientific imaging using low-cost thermal imaging as an example. Our work shows how scientific imaging can open a whole new world previously unseen and enable people to make their own scientific discoveries. The technology is particularly useful in physical sciences. For instance, chemists often use visually striking color changes shown by pH, redox, and other indicators to detect or track reactions. Thermal imaging represents a novel class of universal indicators that use false color heat maps to visualize chemical processes.

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IFI is joining the no-code movement by developing a conceptual programming environment that allows students to solve complex computational problems in math, science, and engineering without having to write a single line of code. Through this work, IFI is committed to broadening student participation in computer science with an innovative technology that can lower the programming barrier by focusing students on concepts, rather than syntaxes, of computational problems.

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Based on computational physics, Energy2D is an interactive multiphysics simulation program that models all three modes of heat transfer — conduction, convection, and radiation, and their coupling with particle dynamics. Energy2D runs quickly on most computers and eliminates the need to switch among preprocessors, solvers, and postprocessors in order to perform typical computational fluid dynamics simulations. It allows you to design “computational experiments” to test a scientific hypothesis or solve an engineering problem without resorting to complex mathematics.

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