This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: A new AI system that NYU Fire Research Group describes as a digital twin of an active wildfire has, in early testing, shown the ability to forecast how a fire will spread in near real time. It continuously updates its forecast as new data arrives from satellites, weather feeds, terrain, fuel, wildland–urban interface, structures, critical infrastructure, vegetation maps, drones, ground sensors and crews in the field.
The lead research team, comprising research associate professor Prabodh Panindre, research scientist Anandu Nair and global network professor Sunil Kumar, says the system is designed to address one of the most urgent challenges in wildfire response: giving incident commanders timely, explainable forecasts as fire conditions change. The platform displays a U.S. map on which a user picks a fire location, sets the size of the area to forecast and chooses a time horizon of up to 12 hours, updated at set intervals. The map then shows the predicted perimeter of the fire's spread under those criteria.
An AI-enabled plain-language interface, similar to a chatbot, lets users ask direct questions, such as what happens to the eastern flank if the wind shifts north, where to cut a firebreak or which zones to evacuate first. It recommends containment strategies, evacuation plans and training scenarios, citing fire department procedures and published research so commanders can review the reasoning behind its advice. The system works by pairing a physics-informed fire-spread model with an AI layer that continually solves equations governing fire propagation, combustion, heat transfer, fuel type and properties, wind, slope, weather and known fire-transition thresholds, using live conditions rather than relying only on historical fire data, as many machine-learning tools do.
"Our system is not built from training data. It is grounded in physics," said Kumar, who, like Panindre, holds appointments at NYU Abu Dhabi and NYU Tandon School of Engineering. "We solve the fundamental equations of how fire burns and spreads using AI, instead of solving them only in the conventional way." Physics-based simulators can be accurate but computationally intensive, often taking hours to update as conditions change.
Machine-learning tools can be faster but are only as strong as their training data and may struggle under conditions they have not seen before. The digital twin is designed to combine the strengths of both approaches, continuously synchronizing with new observations to produce updated forecasts in near real time. The researchers have filed a provisional U.S. patent through NYU.
The team tested the system on three active wildfires: Oregon's Grasshopper Fire, Utah's Widemouth 2 Fire and New Mexico's Sacaton Fire. In one test involving the Grasshopper Fire, the predicted and observed fire areas achieved a 94.6% match using a standard measure of spatial overlap. Other tests showed varying results, with agreement declining to 72.4% in one Grasshopper Fire forecast following a longer gap between satellite observations.
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