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  1. Propagation of partially spatially coherent laser beams in instantaneous Kerr media

    The propagation of intense, partially spatially coherent laser beams in a medium with instantaneous third-order susceptibility is studied analytically and numerically. For sufficiently high power relative to that required for nonlinear self-focusing, the propagation initially proceeds in two stages. In the first stage, spatial coherence builds up, and in the second stage, the number of speckles reduces. Once the degree of coherence is sufficiently high, whole-beam self-focusing occurs. The beam power is mostly confined within the initial spot radius. Two analytical approaches for describing the evolution of the beam are presented. The method of moments leads to an analytical solutionmore » for the rms spot radius that is in excellent agreement with simulations. This method does not require any knowledge of the field statistics beyond the initial conditions and provides no information about the evolution of the individual speckles. The other approach employs a self-similar solution for the second-order coherence function of the field and assumes that the fourth-order coherence function is factorizable and obeys complex circular Gaussian random statistics. The latter method also leads to an analytical expression for the spot radius, but its predictions for the qualitative evolution of the speckles disagree with wave-optics simulations.« less
  2. Towards Philosophical Reasoning with Agentic LLMs: Socratic Method for Scientific Assistance

    As large language models (LLMs) become central tools in science, improving their reasoning capabilities is critical for meaningful and trustworthy applications. We introduce a Socratic agent for scientific reasoning, implemented through a structured system prompt that guides LLMs via classical principles of inquiry. Unlike typical prompt engineering or retrieval-based methods, our approach leverages definition, analogy, hypothesis elimination, and other Socratic techniques to generate more coherent, critical, and domain-aware responses. We evaluate the agent across diverse scientific domains and benchmark it on the abstraction and reasoning corpus challenge dataset, achieving 97.15% under a fixed prompting protocol and without fine-tuning or externalmore » tools. Expert evaluation shows improved reasoning depth, clarity, and adaptability over conventional LLM outputs, suggesting that structured prompting rooted in philosophical reasoning can improve the scientific utility of language models.« less
  3. Enhanced stimulated Raman scattering during intense laser propagation

    Stimulated Raman scattering is ubiquitous in many high-intensity laser environments. Parametric four-wave mixing between the pump and Raman sidebands can affect the Raman gain, but stringent phase matching requirements and strongly nonlinear dynamics obscure clear understanding of its effects at high laser powers. Here we investigate four-wave mixing in the presence of strong self-focusing and weak ionization at laser powers above the Kerr critical power. Theoretical analysis shows that the plasma generated at focus naturally leads to phase matching conditions suitable for enhanced Raman gain, almost without regard to the initial phase mismatch. Multidimensional nonlinear optical simulations with multiphoton andmore » collisional ionization confirm the enhancement and suggest that it may lead to significantly higher Raman losses in some high-intensity laser environments.« less
  4. Proton acceleration in an overdense hydrogen plasma by intense CO2 laser pulses with nonlinear propagation effects in the underdense pre-plasma

    We report on proton acceleration from intense CO2 laser-irradiated hydrogen plasmas at near-critical densities, with the density gradient steepened by Nd:YAG laser ablation-driven hydrodynamic shocks. While the experimental results, such as the quasi-monoenergetic proton spectra and their scaling with respect to the laser energy, are generally in agreement with the simulations, certain laser shots produced significantly higher proton energies than anticipated during the experiment. The increased proton energy may be linked to nonlinear propagation effects in the steepened plasma density ramp before the critical surface, including relativistic self-focusing and, for the case of temporally-structured laser pulses observed in the experiment,more » focusing of the trailing pulse through the plasma channel formed by the leading pulse 25 ps ahead. The occurrence of channel focusing in the underdense hydrogen plasma is supported by a subsequent pump-probe experiment with a dark-field imaging technique, where the formation of ion channels was observed after the passage of an intense CO2 laser pulse« less
  5. Uncertainty Propagation Analysis of Computational Models in Laser Powder Bed Fusion Additive Manufacturing Using Polynomial Chaos Expansions

    Computational models for simulating physical phenomena during laser-based powder bed fusion additive manufacturing (L-PBF AM) processes are critical for enhancing our understanding of these phenomena, enable process optimization, and accelerate qualification and certification of AM materials and parts. It is a well-known fact that such models typically involve multiple sources of uncertainty that originate from different sources such as model parameters uncertainty, or model/code inadequacy, among many others. Uncertainty quantification (UQ) is a broad field that focuses on characterizing such uncertainties in order to maximize the benefit of these models. Although UQ has been a center theme in computational modelsmore » associated with diverse fields such as computational fluid dynamics and macro-economics, it has not yet been fully exploited with computational models for advanced manufacturing. The current study introduces one among the first efforts to conduct uncertainty propagation (UP) analysis in the context of L-PBF AM. More specifically, we present a generalized polynomial chaos expansions (gPCE) framework to assess the distributions of melt pool dimensions due to uncertainty in input model parameters. We develop the methodology and then employ it to validate model predictions, both through benchmarking them against Monte Carlo (MC) methods and against experimental data acquired from an experimental testbed.« less

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"Johnson, Luke"

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