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In the last several years, record-setting wildfires have released millions of smoke-borne contaminants into the air, setting off air quality alerts across the country. APL is using artificial intelligence to accelerate air quality forecasts and ultimately deliver a better understanding of how and where these pollutants will travel.

Leveraging the power of artificial intelligence and mathematics to spur innovation and novel solutions to challenges at the intersection of climate change and national security

The leading edges of hypersonics vehicles—as well as fins, control surfaces, and apertures—need to be protected against speeds exceeding Mach 5, temperatures well above 1,000 degrees Celsius, oxidation from the atmosphere, and tremendous aerodynamic shear loads. Custom materials can enable mission success in these extreme environments.

Artificial intelligence experts and oceanographers are integrating AI with traditional climate modeling methods to enable scientific researchers to better understand climate tipping points, critical thresholds that, once crossed, could “tip” a natural climate system into an entirely different state.

With the clock ticking down to the first Minuteman III test flight to feature a miniature analog translator (MAT)—developed and built by APL to replace the obsolete full signal translator (FST) used for real-time range tracking and GPS signal data on prior test flights—qualification tests identified a need to change a configuration file in the MAT ground equipment.

Working with technical consultants and the Navy fighter aircraft community, we created a pilot-behavior model for air-to-air combat.

APL is reimagining and accelerating the targeted discovery of materials tailored to withstand and perform in the most demanding conditions, ensuring enhanced capabilities in extreme environments.

APL led key “end-to-end” system-level performance analysis in collaboration with the government–industry team for the SM-3 Block IIA missile, cooperatively developed by the United States and Japan.

Using a combination of machine learning, satellite imagery, and localized emissions data, APL advances its accurate, scalable, and easily configurable greenhouse gas monitoring framework for road transportation.