Related papers: U-Net-based surrogate modeling for attosecond X-ra…
Data assimilation in subsurface flow systems is challenging due to the large number of flow simulations often required, and by the need to preserve geological realism in the calibrated (posterior) models. In this work we present a…
Lasers capable of generating attosecond X-ray pulses in the water window (282 to 533 eV) through high-order harmonic generation are normally based on inefficient, multi-stage optical parametric amplifiers or optical parametric chirped pulse…
X-ray free-electron lasers (XFELs) are the only sources currently able to produce bright few-fs pulses with tunable photon energies from 100 eV to more than 10 keV. Due to the stochastic SASE operating principles and other technical issues…
Influence of a linear energy chirp in the electron beam on a SASE FEL operation is studied analytically and numerically using 1-D model. Explicit expressions for Green's functions and for output power of a SASE FEL are obtained for…
We present a finite element-inspired hypergraph neural network framework for predicting flow-induced vibrations in freely oscillating cylinders. The surrogate architecture transforms unstructured computational meshes into node-element…
A thin and dense plasma layer is created when a sufficiently strong laser pulse impinges on a solid target. The nonlinearity introduced by the time-dependent electron density leads to the generation of harmonics. The pulse duration of the…
Single-shot imaging with femtosecond X-ray lasers is a powerful measurement technique that can achieve both high spatial and temporal resolution. However, its accuracy has been severely limited by the difficulty of applying conventional…
Not being able to understand and predict the behavior of deep learning systems makes it hard to decide what architecture and algorithm to use for a given problem. In science and engineering, modeling is a methodology used to understand…
In many mechanistic medical, biological, physical and engineered spatiotemporal dynamic models the numerical solution of partial differential equations (PDEs) can make simulations impractically slow. Biological models require the…
This paper investigates lung nodule classification by using deep neural networks (DNNs). Hyperparameter optimization in DNNs is a computationally expensive problem, where evaluating a hyperparameter configuration may take several hours or…
Coherent control over electron dynamics in atoms and molecules using high-intensity circularly-polarized laser pulses gives rise to current loops, resulting in the emission of magnetic fields. We propose and demonstrate with ab-initio…
We have calculated the resonant and nonresonant contributions to attosecond impulsive stimulated electronic Raman scattering (SERS) in regions of autoionizing transitions. Comparison with Multiconfiguration Time-Dependent Hartree-Fock…
We derive the analytical theory describing the process of sub-femtosecond pulse formation from a quasi-monochromatic seeding extreme ultraviolet (XUV) radiation, which propagates in active medium of a hydrogen-like plasma-based X-ray laser…
The explosive growth of data and its related energy consumption is pushing the need to develop energy-efficient brain-inspired schemes and materials for data processing and storage. Here, we demonstrate experimentally that Co/Pt films can…
We report theoretical calculations of high-order harmonic generation (HHG) of Xe with the inclusion of multi-electron effects and macroscopic propagation of the fundamental and harmonic fields in an ionizing medium. By using the…
Surrogate models are effective tools for accelerated design of complex systems. The result of a design optimization procedure using surrogate models can be used to initialize an optimization routine using the full order system. High…
Plasma kinetic equations exhibit pronounced sensitivity to microscopic perturbations in model parameters and data, making reliable and efficient uncertainty quantification (UQ) essential for predictive simulations. However, the cost of…
In NeuroEvolution, the topologies of artificial neural networks are optimized with evolutionary algorithms to solve tasks in data regression, data classification, or reinforcement learning. One downside of NeuroEvolution is the large amount…
We theoretically investigate the generation of higher harmonics and the construction of a single attosecond pulse by means of two oppositely polarized sinc-shaped driver pulses. In comparison to a few-cycle Gaussian pulse of the same…
The topology optimization of artificial neural networks can be particularly difficult if the fitness evaluations require expensive experiments or simulations. For that reason, the optimization methods may need to be supported by surrogate…