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Inverse Modeling for Laser Pulse Shape Design in Inertial Confinement Fusion

NeurIPS 2026 · Probabilistic and scientific modeling › Molecules and proteins · approach: Machine learning for physical simulation

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AI-extracted summary. Written by AI from the paper's abstract only. Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Proposes an inverse-modeling approach to design laser pulse shapes for inertial confinement fusion, as an alternative to expensive simulation-based, manually tuned LP design.

Problem

Laser pulse shape (LP) design for inertial confinement fusion (ICF) currently relies on computationally expensive simulations and manual tuning under stringent physical constraints.

Approach

Frames LP design as an inverse problem: learning to map desired implosion outcomes back to laser pulse shapes, rather than forward-simulating candidate pulses. Details beyond the abstract are not available.

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