arXiv subjects

Bo Guo

Publications and source records attributed to Bo Guo.

15 recordsLinked to original sources

arXiv2026

Field deployment of a laser wakefield accelerator for on-site application

Successive innovations in particle accelerators have continually expanded the frontiers of scientific discovery. Laser wakefield accelerators promise to transform science, medicine, and industry, yet moving them from laboratory demonstrations to reliable real-world operation has remained a central, long-standing challenge. Here we report a field-deployable system that produced 100-MeV-class electron beams with 1%-level energy stability during 72 hours of continuous operation and supported routine full-power use throughout a seven-month field trial in an industrial setting. Applied to in situ micro-nondestructive testing, the system generated tens-of-MeV bremsstrahlung X-rays that enabled three-dimensional microtomography of dense materials at sub-50-{\mu}m spatial resolution and revealed 100-{\mu}m-scale internal defects in large composite structures, extending the capabilities beyond those of existing high-energy X-ray sources. These results mark a transition of laser wakefield acceleration from laboratory proof of concept toward practical deployment in scientific and industrial applications.

physics.acc-ph↗

arXiv2026

Bright dual-pulse betatron X-ray generation from a laser wakefield accelerator

Pump-probe experiments using dual ultrashort X-ray pulses provide unique opportunities for resolving non-equilibrium dynamics initiated by intense X-ray excitation. Betatron radiation from laser wakefield accelerators offers femtosecond duration, micrometer-scale source size, and intrinsic synchronization with the driving laser, making it a promising candidate for compact ultrafast X-ray sources. Here, we experimentally demonstrate a high-flux, dual-pulse betatron X-ray source based on a density-tailored gas-mixture target. Two electron bunches are generated within a single plasma wakefield through ionization-induced and shock-front-triggered injection, subsequently producing twin X-ray pulses. The measured electron spectra and dual-component X-ray angular profiles, together with particle-in-cell simulations, identify the contributions of the two electron populations to the radiation. The total X-ray photon yield reaches the level of 10^{10} photons per shot with a 40-TW laser system. These results establish a compact, single-stage route toward high-flux dual-pulse betatron sources for laboratory-scale ultrafast X-ray spectroscopy.

physics.plasm-ph↗

arXiv2026

Absolute charge calibration of DRZ phosphor screens for relativistic electron bunches

Laser-plasma accelerators have been the subject of extensive research in recent years. The electron beams they generate exhibit a broad energy spread. To conveniently characterize beams from laser wakefield acceleration (LWFA), electron spectrometers employing scintillating screens coupled with CCD cameras are typically used. In this work, we calibrate a series of DRZ phosphor screens and measure the spectra of the light they emit. The calibration was performed using the radio-frequency linear electron accelerator at Tsinghua University, which provided monoenergetic electron beams with peak energy of approximately 30 MeV.

physics.acc-ph↗

arXiv2026

VeriAgent: A Tool-Integrated Multi-Agent System with Evolving Memory for PPA-Aware RTL Code Generation

LLMs have recently demonstrated strong capabilities in automatic RTL code generation, achieving high syntactic and functional correctness. However, most methods focus on functional correctness while overlooking critical physical design objectives, including Power, Performance, and Area. In this work, we propose a PPA-aware, tool-integrated multi-agent framework for high-quality verilog code generation. Our framework explicitly incorporates EDA tools into a closed-loop workflow composed of a \textit{Programmer Agent}, a \textit{Correctness Agent}, and a \textit{PPA Agent}, enabling joint optimization of functional correctness and physical metrics. To support continuous improvement without model retraining, we introduce an \textit{Evolved Memory Mechanism} that externalizes optimization experience into structured memory nodes. A dedicated memory manager dynamically maintains the memory pool and allows the system to refine strategies based on historical execution trajectories. Extensive experiments demonstrate that our approach achieves strong functional correctness while delivering significant improvements in PPA metrics. By integrating tool-driven feedback with structured and evolvable memory, our framework transforms RTL generation from one-shot reasoning into a continual, feedback-driven optimization process, providing a scalable pathway for deploying LLMs in real-world hardware design flows.

cs.CL↗

arXiv2026

Frequency downshifting stair for ultra-intense femtosecond lasers through a plasma-photonics structure

Wavelength-tunable ultra-intense femtosecond lasers may enable breakthroughs in diverse areas of science spanning attosecond science, particle acceleration and beyond. Conventional crystal-based methods are limited by gain bandwidth and damage thresholds, which restrict their wavelength tunability. Plasma-based frequency conversion, unconstrained by material damage, offers a promising alternative. Here, a novel scheme named Frequency Downshifting Stair (FDS) based on plasma bubble filling control is presented. The FDS enables arbitrary frequency down-conversion of ultra-intense femtosecond pulses and yields chirp-free laser pulses. It can achieve near-100% photon conversion efficiency, approaching the physical limit. This is attributed to the linear control by the FDS of laser chirp evolution during the photon deceleration in the plasma wake bubble. For a laser pulse with an arbitrary wavelength {\lambda}_0 (e.g., {\lambda}_0=800nm), proof-of-concept PIC simulations demonstrate that a single-stage FDS enables continuous wavelength tuning from {\lambda}_0 to {2{\lambda}}_0 (800-1600nm). Moreover, a three-stage cascaded FDS achieves more than tenfold frequency (10{\lambda}_0) downshifting to a central wavelength of 8.5{\mu}m. The FDS scheme thus provides a universal pathway for generating high-energy, few-cycle pulses across the broad infrared regime, offering a powerful new tool for wavelength-dependent ultrafast science.

physics.optics↗

arXiv2026

MERLIN: Building Low-SNR Robust Multimodal LLMs for Electromagnetic Signals

The paradigm of Multimodal Large Language Models (MLLMs) offers a promising blueprint for advancing the electromagnetic (EM) domain. However, prevailing approaches often deviate from the native MLLM paradigm, instead using task-specific or pipelined architectures that lead to fundamental limitations in model performance and generalization. Fully realizing the MLLM potential in EM domain requires overcoming three main challenges: (1) Data. The scarcity of high-quality datasets with paired EM signals and descriptive text annotations used for MLLMs pre-training; (2) Benchmark. The absence of comprehensive benchmarks to systematically evaluate and compare the performance of models on EM signal-to-text tasks; (3) Model. A critical fragility in low Signal-to-Noise Ratio (SNR) environments, where critical signal features can be obscured, leading to significant performance degradation. To address these challenges, we introduce a tripartite contribution to establish a foundation for MLLMs in the EM domain. First, to overcome data scarcity, we construct and release EM-100k, a large-scale dataset comprising over 100,000 EM signal-text pairs. Second, to enable rigorous and standardized evaluation, we propose EM-Bench, the most comprehensive benchmark featuring diverse downstream tasks spanning from perception to reasoning. Finally, to tackle the core modeling challenge, we present MERLIN, a novel training framework designed not only to align low-level signal representations with high-level semantic text, but also to explicitly enhance model robustness and performance in challenging low-SNR environments. Comprehensive experiments validate our method, showing that MERLIN is state-of-the-art in the EM-Bench and exhibits remarkable robustness in low-SNR settings.

cs.CV↗

arXiv2026

Coupled two-phase flow and surfactant/PFAS transport in porous media with angular pores: From pore-scale physics to Darcy-scale modeling

Two-phase surfactant-laden flow and transport in porous media are central to many natural and engineering applications. Surfactants alter two-phase flow by modifying interfacial tension and wettability, while two-phase flow controls surfactant transport pathways and interfacial adsorption. These coupled processes are commonly modeled using Darcy-type two-phase flow equations combined with advection--dispersion--adsorption transport equations, with capillary pressure--saturation relationships scaled by the Leverett $J$-function. However, the Leverett $J$-function idealizes porous media as bundles of cylindrical tubes and decouples interfacial tension and wettability, limiting its ability to represent angular pore geometries and interfacial tension--wettability coupling effects. We present a modeling framework that explicitly incorporates pore angularity and interfacial tension--wettability coupling into Darcy-scale surfactant-laden flow and transport models. Two-phase flow properties are derived for angular pores, upscaled across pore size distributions, and formulated as explicit and closed-form expressions. These upscaled relationships are integrated into a coupled flow--transport model to simulate transient two-phase flow and surfactant transport. Results reveal a nonlinear and nonmonotonic dependence of two-phase flow properties on pore angularity, pore size distribution, and interfacial tension. Example simulations of water flow and PFAS migration in unsaturated soils indicate that surfactant-induced flow effects on PFAS leaching are generally minor under typical conditions, whereas pore angularity strongly controls water flow, interfacial area, and PFAS retention. Overall, the proposed framework provides a more physically grounded approach for modeling two-phase surfactant-laden flow and transport in porous media.

physics.flu-dyn↗

arXiv2025

PolarGS: Polarimetric Cues for Ambiguity-Free Gaussian Splatting with Accurate Geometry Recovery

Recent advances in surface reconstruction for 3D Gaussian Splatting (3DGS) have enabled remarkable geometric accuracy. However, their performance degrades in photometrically ambiguous regions such as reflective and textureless surfaces, where unreliable cues disrupt photometric consistency and hinder accurate geometry estimation. Reflected light is often partially polarized in a manner that reveals surface orientation, making polarization an optic complement to photometric cues in resolving such ambiguities. Therefore, we propose PolarGS, an optics-aware extension of RGB-based 3DGS that leverages polarization as an optical prior to resolve photometric ambiguities and enhance reconstruction accuracy. Specifically, we introduce two complementary modules: a polarization-guided photometric correction strategy, which ensures photometric consistency by identifying reflective regions via the Degree of Linear Polarization (DoLP) and refining reflective Gaussians with Color Refinement Maps; and a polarization-enhanced Gaussian densification mechanism for textureless area geometry recovery, which integrates both Angle and Degree of Linear Polarization (A/DoLP) into a PatchMatch-based depth completion process. This enables the back-projection and fusion of new Gaussians, leading to more complete reconstruction. PolarGS is framework-agnostic and achieves superior geometric accuracy compared to state-of-the-art methods.

cs.CV↗

arXiv2025

SDRNET: Stacked Deep Residual Network for Accurate Semantic Segmentation of Fine-Resolution Remotely Sensed Images

Land cover maps generated from semantic segmentation of high-resolution remotely sensed images have drawn mucon in the photogrammetry and remote sensing research community. Currently, massive fine-resolution remotely sensed (FRRS) images acquired by improving sensing and imaging technologies become available. However, accurate semantic segmentation of such FRRS images is greatly affected by substantial class disparities, the invisibility of key ground objects due to occlusion, and object size variation. Despite the extraordinary potential in deep convolutional neural networks (DCNNs) in image feature learning and representation, extracting sufficient features from FRRS images for accurate semantic segmentation is still challenging. These challenges demand the deep learning models to learn robust features and generate sufficient feature descriptors. Specifically, learning multi-contextual features to guarantee adequate coverage of varied object sizes from the ground scene and harnessing global-local contexts to overcome class disparities challenge even profound networks. Deeper networks significantly lose spatial details due to gradual downsampling processes resulting in poor segmentation results and coarse boundaries. This article presents a stacked deep residual network (SDRNet) for semantic segmentation from FRRS images. The proposed framework utilizes two stacked encoder-decoder networks to harness long-range semantics yet preserve spatial information and dilated residual blocks (DRB) between each encoder and decoder network to capture sufficient global dependencies thus improving segmentation performance. Our experimental results obtained using the ISPRS Vaihingen and Potsdam datasets demonstrate that the SDRNet performs effectively and competitively against current DCNNs in semantic segmentation.

cs.CV↗

arXiv2023

Experimental demonstration of mice tumor control with a laser-accelerated high-energy electron radiotherapy prototype

Radiotherapy using very-high-energy electron (VHEE) beams (50-300 MeV) has attracted considerable attention due to its advantageous dose deposition characteristics, enabling deep penetration and the potential for ultra-high dose rate treatment. One promising approach to compactly delivering these high energy electron beams in a cost-effective manner is laser wakefield acceleration (LWFA), which offers ultra-strong accelerating gradients. However, the transition from this concept to a functional machine intended for tumor treatment is still being investigated. Here we present the first self-developed prototype for LWFA-based VHEE radiotherapy, exhibiting high compactness (occupying less than 5 square meters) and high operational stability (validated over a period of one month). Subsequently, we employed this device to irradiate a tumor implanted in a mouse model. Following a dose delivery of $5.8\pm0.2$ Gy with precise tumor conformity, all irradiated mice exhibited pronounced control of tumor growth. For comparison, this tumor-control efficacy was similar to that achieved using commercial X-ray radiotherapy equipment operating at equivalent doses. These results demonstrate the potential of a compact laser-driven VHEE system for preclinical studies involving small animal models and its promising prospects for future clinical translation in cancer therapy.

physics.med-ph↗

arXiv2023

PBWR: Parametric Building Wireframe Reconstruction from Aerial LiDAR Point Clouds

In this paper, we present an end-to-end 3D building wireframe reconstruction method to regress edges directly from aerial LiDAR point clouds.Our method, named Parametric Building Wireframe Reconstruction (PBWR), takes aerial LiDAR point clouds and initial edge entities as input, and fully uses self-attention mechanism of transformers to regress edge parameters without any intermediate steps such as corner prediction. We propose an edge non-maximum suppression (E-NMS) module based on edge similarityto remove redundant edges. Additionally, a dedicated edge loss function is utilized to guide the PBWR in regressing edges parameters, where simple use of edge distance loss isn't suitable. In our experiments, we demonstrate state-of-the-art results on the Building3D dataset, achieving an improvement of approximately 36% in entry-level dataset edge accuracy and around 42% improvement in the Tallinn dataset.

cs.CV↗

arXiv2023

Graph Convolutional Networks for Simulating Multi-phase Flow and Transport in Porous Media

Numerical simulation of multi-phase fluid dynamics in porous media is critical for many energy and environmental applications in Earth's subsurface. Data-driven surrogate modeling provides computationally inexpensive alternatives to high-fidelity numerical simulators. While the commonly used convolutional neural networks (CNNs) are powerful in approximating partial differential equation solutions, it remains challenging for CNNs to handle irregular and unstructured simulation meshes. However, simulation models for Earth's subsurface often involve unstructured meshes with complex mesh geometries, which limits the application of CNNs. To address this challenge, we construct surrogate models based on Graph Convolutional Networks (GCNs) to approximate the spatial-temporal solutions of multi-phase flow and transport processes in porous media. We propose a new GCN architecture suited to the hyperbolic character of the coupled PDE system, to better capture transport dynamics. Results of 2D heterogeneous test cases show that our surrogates predict the evolutions of pressure and saturation states with high accuracy, and the predicted rollouts remain stable for multiple timesteps. Moreover, the GCN-based models generalize well to irregular domain geometries and unstructured meshes that are unseen in the training dataset.

physics.comp-ph↗

arXiv2022

Ultrafast Electron Diffraction with MeV Electron Source from a Laser Wakefield Accelerator

MeV ultrafast electron diffraction (UED) is a widely used technology for ultrafast structural dynamic studies of matters in numerous areas. The development of laser wakefield accelerator (LWFA) envisions great potential of advanced all-optical electron source based on LWFA in UED applications. We experimentally demonstrated that an LWFA-based device with a miniaturized permanent magnet beamline can generate and manipulate electron beams suitable for UED. In the beam transmission, the LWFA electron beams with intrinsic short duration stretch due to energy spread and then are compressed by a following double bend achromat. The optimized double bend achromat can make the beamline isochronous such that the arrival time jitter induced by the shot-to-shot energy fluctuation can be eliminated, and allow the advantage of the natural laser-beam synchronization for LWFAs to emerge. With the energy filtering, the beam energy spread can be reduced to 3% (FWHM) while a sufficient amount of charge (11.9 fC) per bunch for diffraction is retained. Start-to-end simulations showed that the bunch length reaches ~30 fs (rms) with the same experimental configuration. Clear single-shot and multi-shot diffraction patterns of single-crystalline gold samples are obtained and the derived lattice constant agrees excellently with the real value. Our proof-of-principle experiments open the door to the detection of ultrafast structural dynamics using MeV LWFA beams, and pave the way for the UED applications with sub-10-fs temporal resolution.

physics.acc-ph↗

arXiv2021

Spontaneous imbibition in porous media: from pore scale to Darcy scale

Spontaneous imbibition has been receiving much attention due to its significance in many subsurface and industrial applications. Unveiling pore-scale wetting dynamics, and particularly its upscaling to the Darcy scale are still unresolved. In this work, we conduct image-based pore-network modeling of cocurrent spontaneous imbibition and the corresponding quasi-static imbibition, in homogeneous sintered glass beads as well as heterogeneous Estaillades. A wide range of viscosity ratios and wettability conditions are taken into account. Based on our pore-scale results, we show the influence of pore-scale heterogeneity on imbibition dynamics and nonwetting entrapment. We elucidate different pore-filling mechanisms in imbibition, which helps us understand wetting dynamics. Most importantly, we develop a non-equilibrium model for relative permeability of the wetting phase, which adequately incorporates wetting dynamics. This is crucial to the final goal of developing a two-phase imbibition model with measurable material properties such as capillary pressure and relative permeability. Finally, we propose some future work on both numerical and experimental verifications of the developed non-equilibrium permeability model.

cond-mat.soft↗

arXiv2017

An adaptive multiphysics model coupling vertical equilibrium and full multidimensions for multiphase flow in porous media

Efficient multiphysics models that can adapt to the varying complexity of physical processes in space and time are desirable for modeling fluid migration in the subsurface. Vertical equilibrium (VE) models are simplified mathematical models that are computationally efficient but rely on the assumption of instant gravity segregation of the two phases, which may not be valid at all times or at all locations in the domain. Here, we present a multiphysics model that couples a VE model to a full multidimensional model that has no reduction in dimensionality. We develop a criterion that determines subdomains where the VE assumption is valid during simulation. The VE model is then adaptively applied in those subdomains, reducing the number of computational cells due to the reduction in dimensionality, while the rest of the domain is solved by the full multidimensional model. We analyze how the threshold parameter of the criterion influences accuracy and computational cost of the new multiphysics model and give recommendations for the choice of optimal threshold parameters. Finally, we use a test case of gas injection to show that the adaptive multiphysics model is much more computationally efficient than using the full multidimensional model in the entire domain, while maintaining much of the accuracy.

physics.comp-ph↗