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Addressing the charged particle beam diagnostics in accelerators poses a formidable challenge, demanding high-fidelity simulations in limited computational time. Machine learning (ML) based surrogate models have emerged as a promising tool…
Modeling the dynamic behavior of deformable objects is crucial for creating realistic digital worlds. While conventional simulations produce high-quality motions, their computational costs are often prohibitive. Subspace simulation…
Representing 3D shape is a fundamental problem in artificial intelligence, which has numerous applications within computer vision and graphics. One avenue that has recently begun to be explored is the use of latent representations of…
Progress in optical techniques has made precision control of the phase profile in optical pulses common and accessible in scientific laboratories. Carefully shaping the field profile of a laser pulse can be used to master the dynamics of…
We propose a new class of waveform foundation models that departs from conventional sequence based representations by modeling physiological time series as realizations of latent event processes. Rather than treating signals as collections…
Space-time structured laser pulses feature an intensity peak that can travel at an arbitrary velocity while maintaining a near-constant profile. These pulses can propagate in uniform media, where their frequencies are correlated with…
A laser is not necessarily a sophisticated device: Pumping energy into an amplifying medium randomly filled with scatterers, a powder for instance, makes a perfect "random laser." In such a laser, the absence of mirrors greatly simplifies…
The dynamics of transient electric fields generated by the interaction of high intensity laser pulses with underdense plasmas has been studied experimentally with the proton projection imaging technique. The formation of a charged channel,…
The ability to accurately model random fields plays a critical role in science and engineering for problems involving uncertain, spatially-varying quantities such as heterogeneous material properties and turbulent flows. Deep generative…
Self-focusing and guiding of ultra-short (pulse duration: L<lambda_P: plasma wavelength), intense laser pulses in underdense plasma relevant to laser wakefield electron acceleration has been studied numerically. The analysis considers…
The mode dynamics of a random laser is investigated in experiment and theory. The laser consists of a ZnCdO/ZnO multiple quantum well with air-holes that provide the necessary feedback. Time-resolved measurements reveal multimode spectra…
Compton scattering of short and ultra short (sub-cycle) laser pulses off mildly relativistic electrons is considered within a QED framework. The temporal shape of the pulse is essential for the differential cross section as a function of…
We introduce the first learning-based method for recovering shapes from Laplacian spectra. Given an auto-encoder, our model takes the form of a cycle-consistent module to map latent vectors to sequences of eigenvalues. This module provides…
Deep generative models are universal tools for learning data distributions on high dimensional data spaces via a mapping to lower dimensional latent spaces. We provide a study of latent space geometries and extend and build upon previous…
Learning useful representations of complex data has been the subject of extensive research for many years. With the diffusion of Deep Neural Networks, Variational Autoencoders have gained lots of attention since they provide an explicit…
Noting the importance of the latent variables in inference and learning, we propose a novel framework for autoencoders based on the homeomorphic transformation of latent variables, which could reduce the distance between vectors in the…
We present detailed experimental and numerical studies of random lasing in weakly scattering systems. The interference of scattered light, which is weak in the passive systems, is greatly enhanced in the presence of high gain, providing…
Recent advances in deep learning have been providing non-intuitive solutions to various inverse problems in optics. At the intersection of machine learning and optics, diffractive networks merge wave-optics with deep learning to design…
We investigate the extension of self-injecting laser wakefield experiments to the regime that will be accessible with the next generation of petawatt class ultra-short pulse laser systems. Using linear scalings, current experimental trends…
Characterization of self-consistent laser-plasma evolutions serves as a fundamental issue in the field of relativistic laser-plasma interactions. In this paper, we present an analysis framework for description of these evolutions during…