arXiv subjects

Chengkun Huang

Publications and source records attributed to Chengkun Huang.

11 recordsLinked to original sources

arXiv2025

A Machine Learning-Driven Solution for Denoising Inertial Confinement Fusion Images

Neutron imaging is essential for diagnosing and optimizing inertial confinement fusion implosions at the National Ignition Facility. Due to the required 10-micrometer resolution, however, neutron image require image reconstruction using iterative algorithms. For low-yield sources, the images may be degraded by various types of noise. Gaussian and Poisson noise often coexist within one image, obscuring fine details and blurring the edges where the source information is encoded. Traditional denoising techniques, such as filtering and thresholding, can inadvertently alter critical features or reshape the noise statistics, potentially impacting the ultimate fidelity of the iterative image reconstruction pipeline. However, recent advances in synthetic data production and machine learning have opened new opportunities to address these challenges. In this study, we present an unsupervised autoencoder with a Cohen-Daubechies- Feauveau (CDF 97) wavelet transform in the latent space, designed to suppress for mixed Gaussian-Poisson noise while preserving essential image features. The network successfully denoises neutron imaging data. Benchmarking against both simulated and experimental NIF datasets demonstrates that our approach achieves lower reconstruction error and superior edge preservation compared to conventional filtering methods such as Block-matching and 3D filtering (BM3D). By validating the effectiveness of unsupervised learning for denoising neutron images, this study establishes a critical first step towards fully AI-driven, end-to-end reconstruction frameworks for ICF diagnostics.

cs.CV↗

arXiv2025

On the Impact of Monte Carlo Statistical Uncertainty on Surrogate-based Design Optimization

In multi-objective design tasks, the computational cost increases rapidly when high-fidelity simulations are used to evaluate objective functions. Surrogate models help mitigate this cost by approximating the simulation output, simplifying the design process. However, under high uncertainty, surrogate models trained on noisy data can produce inaccurate predictions, as their performance depends heavily on the quality of training data. This study investigates the impact of data uncertainty on two multi-objective design problems modelled using Monte Carlo transport simulations: a neutron moderator and an ion-to-neutron converter. For each, a grid search was performed using five different tally uncertainty levels to generate training data for neural network surrogate models. These models were then optimized using NSGA-III. The recovered Pareto-fronts were analyzed across uncertainty levels, and the impact of training data quality on optimization outcomes was quantified. Average simulation times were also compared to evaluate the trade-off between accuracy and computational cost. Results show that the influence of simulation uncertainty is strongly problem-dependent. In the neutron moderator case, higher uncertainties led to exaggerated objective sensitivities and distorted Pareto-fronts, reducing normalized hypervolume. In contrast, the ion-to-neutron converter task was less affected--low-fidelity simulations produced results similar to those from high-fidelity data. These findings suggest that a fixed-fidelity approach is not optimal. Surrogate models can still recover the Pareto-front under noisy conditions, and multi-fidelity studies can help identify the appropriate uncertainty level for each problem, enabling better trade-offs between computational efficiency and optimization accuracy.

stat.AP↗

arXiv2025

Novel Methods for Simulating Astrophysical Plasmas and the Coherent Emission in Fast Radio Bursts

We present particle-in-cell simulations of one dimensional relativistic electromagnetic shocks in a uniform magnetic field, for a range of magnetic field strengths, plasma temperatures and numerical initial conditions. We show that the particle energy distributions of these shocks can develop a state of population inversion in the precursor and shock regions, which may allow for synchrotron maser (or maser-like, coherent) emission. Our set-up is applicable to conditions expected in models of fast radio bursts and therefore lends credence to the synchrotron maser model for these transients. We also show, for the first time, how a newly developed ``analytic particle pusher'' for kinetic simulations gives similar results to the commonly-used Boris pusher, but for larger timesteps and without the need to resolve the gyro-radius and gyro-period of the system. This has important implications for modeling astrophysical plasmas in extreme magnetic fields as well as for bridging scales between kinetic and fluid regimes.

astro-ph.HE↗

arXiv2024

A first-principles study of structural, elastic, electronic, and transport properties of Cs2Te

The pursuit to operate photocathodes at high accelerating gradients to increase brightness of electron beams is gaining interests within the accelerator community. Cesium telluride (Cs2Te) is a widely used photocathode material and it is presumed to offer resilience to higher gradients because of its wider band gap compared to other semiconductors. Despite its advantages, crucial material properties of Cs2Te remain largely unknown both in theory and experiments. In this study, we employ first-principles calculations to provide detailed structural, elastic, electronic and transport properties of Cs2Te. It is found that Cs2Te has an intrinsic mobility of 20 cm2/Vs for electrons and 2.0 cm2/Vs for holes at room temperature. The low mobility is primarily limited by the strong polar optical phonon scattering. Cs2Te also exhibits ultralow lattice thermal conductivity of 0.2 W/(m*K) at room temperature. Based on the energy gain/loss balance under external field and electron-phonon scattering, we predict that Cs2Te has a dielectric breakdown field in the range from ~60 MV/m to ~132 MV/m at room temperature dependent on the doping level of Cs2Te. Our results are crucial to advance the understanding of applicability of Cs2Te photocathodes for high-gradient operation.

cond-mat.mtrl-sci↗

arXiv2024

Physics-informed Meta-instrument for eXperiments (PiMiX) with applications to fusion energy

Data-driven methods (DDMs), such as deep neural networks, offer a generic approach to integrated data analysis (IDA), integrated diagnostic-to-control (IDC) workflows through data fusion (DF), which includes multi-instrument data fusion (MIDF), multi-experiment data fusion (MXDF), and simulation-experiment data fusion (SXDF). These features make DDMs attractive to nuclear fusion energy and power plant applications, leveraging accelerated workflows through machine learning and artificial intelligence. Here we describe Physics-informed Meta-instrument for eXperiments (PiMiX) that integrates X-ray (including high-energy photons such as $γ$-rays from nuclear fusion), neutron and others (such as proton radiography) measurements for nuclear fusion. PiMiX solves multi-domain high-dimensional optimization problems and integrates multi-modal measurements with multiphysics modeling through neural networks. Super-resolution for neutron detection and energy resolved X-ray detection have been demonstrated. Multi-modal measurements through MIDF can extract more information than individual or uni-modal measurements alone. Further optimization schemes through DF are possible towards empirical fusion scaling laws discovery and new fusion reactor designs.

physics.data-an↗

arXiv2022

Physics-Informed Machine Learning for Modeling Turbulence in Supernovae

Turbulence plays an important role in astrophysical phenomena, including core-collapse supernovae (CCSN), but current simulations must rely on subgrid models since direct numerical simulation (DNS) is too expensive. Unfortunately, existing subgrid models are not sufficiently accurate. Recently, Machine Learning (ML) has shown an impressive predictive capability for calculating turbulence closure. We have developed a physics-informed convolutional neural network (CNN) to preserve the realizability condition of Reynolds stress that is necessary for accurate turbulent pressure prediction. The applicability of the ML subgrid model is tested here for magnetohydrodynamic (MHD) turbulence in both the stationary and dynamic regimes. Our future goal is to utilize this ML methodology (available on GitHub) in the CCSN framework to investigate the effects of accurately-modeled turbulence on the explosion of these stars.

physics.comp-ph↗

arXiv2020

Fermi-type particle acceleration from magnetic reconnection at the termination shock of a relativistic striped wind

An oblique-rotating pulsar generates a relativistic striped wind in a pulsar wind nebula (PWN). The termination shock of the PWN compresses the Poynting-flux-dominated flow and drives magnetic reconnection. By carrying out particle-in-cell (PIC) simulations of the termination shock of the PWN, we study the shock structure as well as the energy conversion processes and particle acceleration mechanisms. With the recent advances in the numerical methods, we extend the simulations to the ultra-relativistic regime with bulk Lorentz factor up to γ_{0}=10^{6}. Magnetic reconnection at the termination shock is highly efficient at converting magnetic energy to particle kinetic energy and accelerating particles to high energies. We find that the resulting energy spectra crucially depend on λ/d_{e}. When λ/d_{e} is large (λ\gtrsim40d_{e}) , the downstream particle spectra form a power-law distribution in the magnetically dominated relativistic wind regime with upstream magnetization parameter σ_{0}=10. By analyzing particle trajectories and statistical quantities relevant to particle energization, we find that Fermi-type mechanism dominates the particle acceleration and power-law formation. We find that the results for particle acceleration are scalable as γ_{0} and σ_{0} increase to large values. The maximum energy for electrons and positrons can reach hundreds of TeV if the wind has a bulk Lorentz factor γ_{0}\approx10^{6} and magnetization parameter σ_{0}=10, which can explain the recent observations of high-energy gamma-rays from pulsar wind nebulae (PWNe).

astro-ph.HE↗

arXiv2020

Studying particle acceleration from driven magnetic reconnection at the termination shock of a relativistic striped wind using particle-in-cell simulations

A rotating pulsar creates a surrounding pulsar wind nebula (PWN) by steadily releasing an energetic wind into the interior of the expanding shockwave of supernova remnant or interstellar medium. At the termination shock of a PWN, the Poynting-flux-dominated relativistic striped wind is compressed. Magnetic reconnection is driven by the compression and converts magnetic energy into particle kinetic energy and accelerating particles to high energies. We carrying out particle-in-cell (PIC) simulations to study the shock structure as well as the energy conversion and particle acceleration mechanism. By analyzing particle trajectories, we find that many particles are accelerated by Fermi-type mechanism. The maximum energy for electrons and positrons can reach hundreds of TeV.

physics.plasm-ph↗

arXiv2019

An \mathcal{O}(N) Maxwell solver with improved numerical dispersion properties

A Maxwell solver derived from finite element method with \mathcal{O}(N) computing cost is developed to improve the numerical dispersion properties in relativistic particle-in-cell (PIC) simulations. The correction of the dispersion relation of the electromagnetic wave is achieved using the neighboring cells via an iteration scheme without decomposing into Fourier modes. The local nature of the communication is ideally suited to massively parallel computer architectures. This Maxwell solver constrains the Numerical Cherenkov instability (NCI) for the ultra-relativistic drifting pair plasma in x direction to large wave vectors for two dimensional grid. The growth rate of NCI is suppressed by using the low pass filtering.

physics.plasm-ph↗

arXiv2019

Theory of electron transport and emission from a semiconductor nanotip

An effective mass based model accounting for the conduction band quantization in a high aspect ratio semiconductor nanotip is developed to describe injected electron transport and subsequent electron emission from the nanotip. A transfer matrix formalism is used to treat electron scattering induced by the variation in the tip diameter and the electron emission. Numerical analysis of the scattering and emission probabilities is performed for the diamond parametrized nanotip model. Our scattering and emission models are further combined with a Monte Carlo (MC) approach to simulate electron transport through the nanotip. The MC simulations, also accounting for the electron-phonon scattering and externally applied electric field, are performed for a minimal nanotip model and an equivalent width diamond slab. An effect of the level quantization, electron scattering due to the nanotip diameter variation, and electron-phonon scattering on the nanotip emission properties is identified and compared with the case of bulk slab.

cond-mat.mes-hall↗

arXiv2015

Efficient quasi-monoenergetic ion beams up to 18 MeV/nucleon via self-generated plasma fields in relativistic laser plasmas

Table-top laser-plasma ion accelerators seldom achieve narrow energy spreads, and never without serious compromises in efficiency, particle yield, etc. Using massive computer simulations, we identify a self-organizing scheme that exploits persisting self-generated plasma electric (~TV/m) and magnetic (~10${}^{4}$ Tesla) fields to reduce the ion energy spread after the laser exits the plasma - separating the ion acceleration from the energy spread reduction. Consistent with the scheme, we experimentally demonstrate aluminum and carbon ion beams with narrow spectral peaks at energies up to 310 MeV (11.5 MeV/nucleon) and 220 MeV (18.3 MeV/nucleon), respectively, with high conversion efficiency (~5%, i.e., 4J out of 80J laser). This is achieved with 0.12 PW high-contrast Gaussian laser pulses irradiating planar foils with optimal thicknesses of up to 250 nm that scale with laser intensity. When increasing the focused laser intensity fourfold (by reducing the focusing optic f/number twofold), the spectral-peak energy increases twofold. These results pave the way for next generation compact accelerators suitable for applications. For example, 400 MeV (33.3 MeV/nucleon) carbon-ion beam with narrow energy spread required for ion fast ignition could be generated using PW-class lasers.

physics.plasm-ph↗