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For many applications of pulsed radiation, the time-history of the radiation intensity must be optimized to induce a desired time-history of conditions. This optimization is normally performed using multi-physics simulations of the system. The pulse shape is parametrized, and multiple simulations are performed in which the parameters are adjusted until the desired response is induced. These simulations are often computationally intensive, and the optimization by iteration of parameters in forward simulations is then expensive and slow. In many cases, the desired response can be expressed such that an instantaneous difference between the actual and desired response can be calculated. In principle, a computer program used to perform the forward simulation could be modified to adjust the instantaneous radiation drive automaticaly until the desired instantaneous response is achieved. Unfortunately, such modifications may be impracticable in a complicated multi-physics program. However, the computational time increment in such simulations is generally much shorter than the time scale of changes in the desired response. It is much more practicable to adjust the radiation source so that the response tends toward the desired value at later times. This relaxed in-situ optimization method can give an adequate design for a pulse shape in a single forward simulation, giving a typical gain in computational efficiency of tens to thousands. This approach was demonstrated for the design of laser pulse shapes to induce ramp loading to high pressure in target assemblies incorporating ablators of significantly different mechanical impedance than the sample, requiring complicated pulse shaping.
The radiation hydrodynamics equations for smoothed particle hydrodynamics are derived by operator splitting the radiation and hydrodynamics terms, including necessary terms for material motion, and discretizing each of the sets of equations separatel
The quantum approximate optimization algorithm (QAOA) applies two Hamiltonians to a quantum system in alternation. The original goal of the algorithm was to drive the system close to the ground state of one of the Hamiltonians. This paper shows that
We propose a computationally efficient limited memory Covariance Matrix Adaptation Evolution Strategy for large scale optimization, which we call the LM-CMA-ES. The LM-CMA-ES is a stochastic, derivative-free algorithm for numerical optimization of no
Image compression using neural networks have reached or exceeded non-neural methods (such as JPEG, WebP, BPG). While these networks are state of the art in ratedistortion performance, computational feasibility of these models remains a challenge. We
The challenge of assigning importance to individual neurons in a network is of interest when interpreting deep learning models. In recent work, Dhamdhere et al. proposed Total Conductance, a natural refinement of Integrated Gradients for attributing