arXiv subjects

Xingwen Li

Publications and source records attributed to Xingwen Li.

6 recordsLinked to original sources

arXiv2026

Numerical modeling and simulation on nanosecond laser-target interactions

Nanosecond lasers are widely used in industrial applications as they are relatively inexpensive, and their compactness and robustness are an advantage. Much experimental work has been carried out to understand deeper the interaction between the nanosecond laser pulses and the targets, as these are complex, transient processes with spatial inhomogeneities. Beside the experiments, the modeling and numerical simulation on the laser interaction with the target are also crucial for understanding the dynamics of laser-material interactions and for optimizing laser processing applications. In this review, the progress of numerical modeling and simulation on nanosecond laser-target interactions are summarized from the aspects of laser-target interactions and target-plasma interface, laser-plasma interactions and plasma radiation, and numerical models on different scales with artificial intelligence advancing. The laser ablation, mass and energy transfer, and mechanical coupling are discussed in the aspect of the nanosecond laser-target interactions and target-plasma interface. The plasma expansion, plasma ionization and recombination, and plasma radiation are discussed in the aspect of the nanosecond laser-plasma interactions and plasma radiation. Then the numerical advances, including microscopic approaches based on molecular dynamics, mesoscopic approaches based on kinetic and statistical physics, macroscopic approaches based on fluid dynamics, and numerical simulations with machine learning are discussed. Finally, the challenges currently being encountered by numerical modeling and simulation on nanosecond laser-target interactions and its potential development direction are considered.

physics.plasm-ph↗

arXiv2026

Measurement of multiple mechanical properties from multi-dimensional signals in nanosecond laser ablation via PINN

Accurate evaluation of mechanical properties in steels under ageing or service conditions remains a major challenge. We propose a thermo-mechanical coupling framework for nanosecond laser ablation based on energy conservation, which is embedded into a physics-informed neural network (PINN) to enable simultaneous inversion of multiple mechanical properties. A thermo-mechanical coupling coefficient is defined to uniformly describe the dynamic allocation of input laser energy among thermal diffusion, mechanical work and plasma shielding across different deformation stages under laser irradiation. Furthermore, hard-to-measure physical characteristics in the coupled equation are replaced with experimentally accessible features obtained through the simultaneous acquisition of spectroscopic, shockwave and surface-wave signals. Using 210 experimental datasets, the framework simultaneously recovers Young's modulus, yield strength, ultimate tensile strength and micro-Vickers hardness with high accuracy (R2=0.9927, 0.9912, 0.9916 and 0.9959 respectively), significantly outperforming the baseline method (ultrasonic velocity regression for E, R2=0.0012). Comparisons with linear normalization and unconstrained neural networks demonstrate that PINN achieves near-unity accuracy through the embedding of conservation-law constraints. Partial dependency analysis further uncovers the nonlinear coupling laws between input features and mechanical properties. The proposed paradigm, integrating conservation laws, measurable features and physics-informed learning, offers a universal approach for non-contact, high-precision and physically consistent multi-to-multi inversion of multiple material properties under nanosecond laser ablation conditions.

physics.plasm-ph↗

arXiv2026

Physics-informed genetic algorithms (PIGAs) facilitating LIBS spectral normalization with shockwave characteristics

Inspired by physics-informed neural networks (PINNs) inheriting both the interpretability of physical laws and the efficient integration capability of machine learning, we propose a framework based on stoichiometric ablation for LIBS spectral normalization, encoding physical constraints between LIBS intensities and shockwave characteristics (temperature Tshock and pressure P) into optimization algorithms with multiple independent objectives, named physics-informed genetic algorithms (PIGAs). It is characterized by its applicability to the wider laser energy range covering laser-induced breakdown to significant plasma shielding and spectral lines undergoing self-absorption outperforming the widely-used physical linear or multivariate data-driven normalization methods. The home-made end-to-end LAP-RTE codes serves as the benchmark to validate the physical reciprocal-logarithmic transformation and its extensibility to self-absorption spectral lines for PIGAs. Next experimental spectral lines are statistically used to validate PIGAs correction effects, the median RSDs of spectral intensities can be effectively reduced by 85% (corrected by P) and 88% (corrected by Tshock) for 108 Fe I lines, while for 33 Fe II lines, reduced by 77% (corrected by P) and 86% (corrected by Tshock). Seventeen self-absorption lines are also corrected effectively, with RSDs being reduced by 78% (corrected by P) and 89% (corrected by Tshock). Our proposed idea of combining optimization methods to quantify unknown parameters in normalization strategies can also be extended to excavate the correlation between parameters for other low-temperature plasma fields with similar processes.

physics.plasm-ph↗

arXiv2026

Coupling model of metallic target ablation-plasma evolution-radiation under nanosecond laser irradiation

The interaction of nanosecond laser pulses with metallic materials involves multiple complex physical processes. It is challenging to construct a self-consistent model capable of uniformly describing all stages. This work establishes a multi-physics coupling model for pure iron, encompassing laser energy deposition, solid-liquid phase transition, gas-liquid interfacial kinetic transport, plasma expansion and ionization, and spectral radiation. The numerical solution adopts a partition method, utilizing an implicit compact difference scheme for the target and a Mac-Cormack explicit scheme for the plasma. The simulations elucidate the emergence of plasma shielding and its inhibitory effect on the evaporation process, thereby confirming that 81.6% of the early-stage ablation products are transported through a supersonic expansion mode. The model successfully captures the complete evolution of the plasma plume from a high-temperature, highly ionized state to a low-temperature, neutral atomic state. Based on this, spectral calculations demonstrate the dynamic evolution of radiative characteristics from an early stage featuring a strong continuum background dominated by ion lines to a later stage where the continuum attenuates, atomic lines become prominent, and self-absorption appears. The emergence of self-absorption proves the ability of the model to effectively capture the optical thickness effects arising from spatial inhomogeneity within the plasma. Through systematic comparison between experimentally measured spectra and calculated results from the PrismSPECT and NIST LIBS spectral programs, the model presented here achieves the highest comprehensive scores in quantitative evaluations of multiple channels. This validates the necessity and superiority of the full-chain self-consistent modeling approach, especially in describing plasma inhomogeneity and radiation transport.

physics.plasm-ph↗

arXiv2024

Computational Pathology: A Survey Review and The Way Forward

Computational Pathology CPath is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CPath is to develop infrastructure and workflows of digital diagnostics as an assistive CAD system for clinical pathology, facilitating transformational changes in the diagnosis and treatment of cancer that are mainly address by CPath tools. With evergrowing developments in deep learning and computer vision algorithms, and the ease of the data flow from digital pathology, currently CPath is witnessing a paradigm shift. Despite the sheer volume of engineering and scientific works being introduced for cancer image analysis, there is still a considerable gap of adopting and integrating these algorithms in clinical practice. This raises a significant question regarding the direction and trends that are undertaken in CPath. In this article we provide a comprehensive review of more than 800 papers to address the challenges faced in problem design all-the-way to the application and implementation viewpoints. We have catalogued each paper into a model-card by examining the key works and challenges faced to layout the current landscape in CPath. We hope this helps the community to locate relevant works and facilitate understanding of the field's future directions. In a nutshell, we oversee the CPath developments in cycle of stages which are required to be cohesively linked together to address the challenges associated with such multidisciplinary science. We overview this cycle from different perspectives of data-centric, model-centric, and application-centric problems. We finally sketch remaining challenges and provide directions for future technical developments and clinical integration of CPath (https://github.com/AtlasAnalyticsLab/CPath_Survey).

eess.IV↗

arXiv2017

Preconditioned wire array Z-pinches driven by a double pulse current generator

Suppressing of the core-corona structures shows a strong potential as a new breakthrough in the X-ray power production of the wire array Z-pinches. In this letter, the demonstration of suppressing the core-corona structures and its ablation using a novel double pulse current generator "Qin-1" facility is presented. The "Qin-1" facility coupled a ~10 kA 20 ns prepulse generator to a ~ 1 MA 170 ns main current generator. Driven by the prepulse current, the two aluminum wire array were mostly heated to gaseous state rather than the core-corona structures, and the implosion of the aluminum vapors driven by the main current showed no ablation, and no trailing mass. The seeds for the MRT instability formed from the inhomogeneous ablation were suppressed, however, the magneto Rayleigh-Taylor instability during the implosion was still significant and further researches on the generation and development of the magneto Rayleigh-Taylor instabilities of this gasified wire array are needed.

physics.plasm-ph↗