[2603.04606] PDE foundation model-accelerated inverse estimation of system parameters in inertial confinement fusion

Computer Science > Machine Learning arXiv:2603.04606 (cs) [Submitted on 4 Mar 2026] Title:PDE foundation model-accelerated inverse estimation of system parameters in inertial confinement fusion Authors:Mahindra Rautela, Alexander Scheinker, Bradley Love, Diane Oyen, Nathan DeBardeleben, Earl Lawrence, Ayan Biswas View a PDF of the paper titled PDE foundation model-accelerated inverse estimation of system parameters in inertial confinement fusion, by Mahindra Rautela and 6 other authors View PDF HTML (experimental) Abstract:PDE foundation models are typically pretrained on large, diverse corpora of PDE datasets and can be adapted to new settings with limited task-specific data. However, most downstream evaluations focus on forward problems, such as autoregressive rollout prediction. In this work, we study an inverse problem in inertial confinement fusion (ICF): estimating system parameters (inputs) from multi-modal, snapshot-style observations (outputs). Using the open JAG benchmark, which provides hyperspectral X-ray images and scalar observables per simulation, we finetune the PDE foundation model and train a lightweight task-specific head to jointly reconstruct hyperspectral images and regress system parameters. The fine-tuned model achieves accurate hyperspectral reconstruction (test MSE 1.2e-3) and strong parameter-estimation performance (up to R^2=0.995). Data-scaling experiments (5%-100% of the training set) show consistent improvements in both reconstruction and reg...