High-dimensional inverse design of inertial fusion implosions via differentiable simulation

Inertial confinement fusion implosion design requires simultaneous optimisation of strongly coupled target and driver parameters across high-dimensional design spaces. Existing automated design approaches typically rely on non-differentiable radiation-hydrodynamics codes treated as black boxes, making optimisation increasingly expensive as dimensionality grows. In this work, we present a differentiable simulation approach for high-dimensional inverse design of inertial confinement fusion implosions. Automatic differentiation through a differentiable implosion physics model, driven by an external pressure pulse, provides gradients of implosion objectives with respect to design parameters, enabling gradient-based optimisation. The framework is applied to 25 kJ OMEGA-scale direct-drive implosions, optimising 500-parameter laser pulses across sampled target geometries. The optimised pulse recovers a near-isoentropic rise to peak power without that structure being imposed. Neural-network pulse parameterisations are additionally explored as a means of accelerating design-space exploration. These results establish differentiable implosion modelling as a promising tool for ICF design, while motivating further work on adjoint robustness and higher-fidelity differentiable simulators.