Inverse Modeling for Laser Pulse Shape Design in Inertial Confinement Fusion
arXiv:2610.33096 [cs.AI] • Submitted 22 October 2026
Authors: Ricardo Luna Gutierrez, Vineet Gundecha, Rahman Ejaz, Varchas Gopalaswamy, Riccardo Betti, Sahand Ghorbanpour, Aarne Lees, Soumyendu Sarkar
Subjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Systems and Control (eess.SY); High Energy Physics – Experiment (hep-ex)
Comments: Initial version submitted to NeurIPS 2026, pending publication
Abstract
The realization of practical fusion energy remains one of the most pressing unsolved scientific challenges, with far-reaching implications for carbon-free power generation. A critical factor in the success of Inertial Confinement Fusion (ICF) is the design of a Laser Pulse Shape (LPS) capable of optimally driving implosions under stringent physical constraints. Conventional LPS design relies on computationally expensive simulations coupled with manual iterative refinement.
This work introduces the ICF Laser Pulse Shape Design System (LPDS), a generative inverse modeling framework that maps desired outcomes and target pellet configurations directly to optimized laser pulse shapes. Central to the approach is a multi-objective loss function designed to ensure that generated pulses satisfy fundamental physical constraints and experimental requirements. The authors further present constraint-conditioning, inpainting, and gradient-based LP editing mechanisms that enable fine-grained control over specific pulse characteristics during generation. In addition, the study examines the transfer of the framework to a limited set of real experimental shots.
The proposed method establishes a data-driven inverse design framework for laser pulse shape design in ICF, contributing to the advancement of practical and sustainable fusion energy.
Key Contributions
- Generative inverse modeling for LPS design. A framework that directly maps target outcomes and pellet configurations to optimized laser pulse shapes, bypassing costly trial-and-error simulation loops.
- Multi-objective loss function. Encodes fundamental physical constraints and experimental requirements to ensure physically viable pulse shapes.
- Fine-grained controllability. Constraint-conditioning, inpainting, and gradient-based editing allow targeted modification of specific pulse characteristics during generation.
- Transfer to real experiments. Evaluation of the framework's transferability to a limited set of real experimental shots.
Context and Significance (2026)
As the fusion community accelerates toward demonstrated net energy gain and reactor-relevant designs, the bottleneck has increasingly shifted from plasma physics understanding to the speed and reliability of design workflows. AI-driven inverse design—exemplified by LPDS—represents a broader 2026 trend in which generative models and multi-objective optimization are being embedded directly into scientific and engineering pipelines for high-energy-density physics. By reducing reliance on manual iterative refinement, such frameworks promise to shorten design cycles and widen the explorable design space for ICF targets.
Citation
Luna Gutierrez, R., Gundecha, V., Ejaz, R., Gopalaswamy, V., Betti, R.,
Ghorbanpour, S., Lees, A., & Sarkar, S. (2026).
Inverse Modeling for Laser Pulse Shape Design in Inertial Confinement Fusion.
arXiv:2610.33096 [cs.AI]. https://doi.org/10.48550/arXiv.2610.33096
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