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

• URL: http://arxiv.org/abs/2603.04606v1
• Date: Wed, 04 Mar 2026 21:07:43 GMT
• 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,
• Abstract summary: We study an inverse problem in inertial confinement fusion (ICF): estimating system parameters (inputs) from multi-modal, snapshot-style observations (outputs)<n>We finetune the PDE foundation model and train a lightweight task-specific head to jointly reconstruct hyperspectral images and regress system parameters.<n>Experiments show consistent improvements in both reconstruction and regression losses as the amount of training data increases.
• Score: 36.885866084809926
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 regression losses as the amount of training data increases, with the largest marginal gains in the low-data regime. Finally, finetuning from pretrained MORPH weights outperforms training the same architecture from scratch, demonstrating that foundation-model initialization improves sample efficiency for data-limited inverse problems in ICF.

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