The AIFORSTE project of the CEA aims to improve emergency management for atmospheric releases resulting from industrial accidents or malicious acts. We will develop a source estimation method operating in near real-time, enabling a fast and effective response to these critical events where time is always a fundamental constraint.

Given the impossibility of using detailed CFD simulations for immediate intervention, and the inaccuracy of simplified analytical models, this postdoctoral position will be devoted to a hybrid approach based on Physics-Informed Artificial Intelligence.

The core of the work will consist in designing and optimizing deep neural network architectures combining PINNs (Physics-Informed Neural Networks) and Neural Operators (FNO, DeepONet). The objective will be to quasi-instantaneously evaluate the position, intensity, and kinetics of one or several unknown release sources from a limited number of noisy measurements, while respecting the physical conservation laws.

Hosted at CEA-Leti (Grenoble) and co-supervised by CEA-DAM, the project will apply to realistic case studies (urban districts, industrial sites) using data generated through high-fidelity simulation. This 18-month project offers a unique opportunity to publish in leading journals and to help build strategic technological autonomy in a field where security and environmental monitoring are of major importance.