arXiv subjects

Xi Deng

Publications and source records attributed to Xi Deng.

At least 19 recordsLinked to original sources

arXiv2026

A Note on the Consistent-$Q$ Scheme for Odd-Odd Nuclei

Compared with even-even nuclear systems, the dynamical structure of low-lying excited states in odd-odd nuclei poses significantly greater theoretical challenges. The interacting boson fermion fermion model provides an effective framework for capturing the structural properties of odd-odd nuclei. Within this algebraic framework, the consistent-$Q$ scheme, which was widely used to map nuclear shape phase diagram, is extended to odd-odd systems to examine how unpaired nucleons influence level structures and their evolution across prototypical transitional regions. The results demonstrate that the presence of unpaired nucleons does not suppress the critical behavior characterizing level evolution across the U(5)-SU(3), U(5)-O(6) and SU(3)-O(6) shape transitions, suggesting that shape phase transitions remain a fundamental mechanism governing the low-lying structural evolution of odd-odd nuclei in the heavy and intermediately-heavy mass regions.

nucl-th↗

arXiv2026

Operator Learning Using Weak Supervision from Walk-on-Spheres

Training neural PDE solvers is often bottlenecked by expensive data generation or unstable physics-informed neural network (PINN) involving challenging optimization landscapes due to higher-order derivatives. To tackle this issue, we propose an alternative approach using Monte Carlo approaches to estimate the solution to the PDE as a stochastic process for weak supervision during training. Leveraging the Walk-on-Spheres method, we introduce a learning scheme called \emph{Walk-on-Spheres Neural Operator (WoS-NO)} which uses weak supervision from WoS to train any given neural operator. We propose to amortize the cost of Monte Carlo walks across the distribution of PDE instances using stochastic representations from the WoS algorithm to generate cheap, noisy, estimates of the PDE solution during training. This is formulated into a data-free physics-informed objective where a neural operator is trained to regress against these weak supervisions, allowing the operator to learn a generalized solution map for an entire family of PDEs. This strategy does not require expensive pre-computed datasets, avoids computing higher-order derivatives for loss functions that are memory-intensive and unstable, and demonstrates zero-shot generalization to novel PDE parameters and domains. Experiments show that for the same number of training steps, our method exhibits up to 8.75$\times$ improvement in $L_2$-error compared to standard physics-informed training schemes, up to 6.31$\times$ improvement in training speed, and reductions of up to 2.97$\times$ in GPU memory consumption. We present the code at https://github.com/neuraloperator/WoS-NO

cs.LG↗

arXiv2025

From Generic to Specialized: A Subspecialty Diagnostic System Powered by Self-Supervised Learning for Cervical Histopathology

Cervical cancer remains a major malignancy, necessitating extensive and complex histopathological assessments and comprehensive support tools. Although deep learning shows promise, these models still lack accuracy and generalizability. General foundation models offer a broader reach but remain limited in capturing subspecialty-specific features and task adaptability. We introduce the Cervical Subspecialty Pathology (CerS-Path) diagnostic system, developed through two synergistic pretraining stages: self-supervised learning on approximately 190 million tissue patches from 140,000 slides to build a cervical-specific feature extractor, and multimodal enhancement with 2.5 million image-text pairs, followed by integration with multiple downstream diagnostic functions. Supporting eight diagnostic functions, including rare cancer classification and multimodal Q&A, CerS-Path surpasses prior foundation models in scope and clinical applicability. Comprehensive evaluations demonstrate a significant advance in cervical pathology, with prospective testing on 3,173 cases across five centers maintaining 99.38% screening sensitivity and excellent generalizability, highlighting its potential for subspecialty diagnostic translation and cervical cancer screening.

cs.CV↗

arXiv2025

B(E2) anomaly and triaxial deformation within a two-fluid SU(3) symmetry

The correlation between $B(E2)$ structure and triaxial deformation has been investigated within the framework of the proton-neutron boson model. The analysis reveals that the distinctive feature, characterized by $B(E2;4_1^+\rightarrow2_1^+)/B(E2;2_1^+\rightarrow0_1^+)<1.0$ along with $E(4_1^+)/E(2_1^+)>2.0$, can emerge from the triaxial SU(3) symmetry inherent in two-fluid boson systems, attributed to band-mixing effects. This suggests a symmetry-based understanding of the anomalous $E2$ transitions observed in experiments.

nucl-th↗

arXiv2025

A novel hybrid approach for accurate simulation of compressible multi-component flows across all-Mach number

Numerical simulation of multi-component flow systems characterized by the simultaneous presence of pressure-velocity coupling and pressure-density coupling dominated regions remains a significant challenge in computational fluid dynamics. Thus, this work presents a novel approach that combines the Godunov-type scheme for high-speed flows with the projection solution procedure for incompressible flows to address this challenge. The proposed hybrid approach begins by splitting the inviscid flux into the advection part and the pressure part. The solution variables are first updated to their intermediate states by solving the advection part with the all-speed AUSM (Advection Upwind Splitting Method) Riemann solver. The advection flux in AUSM is modified to eliminate the pressure flux term that deteriorates the accuracy at the low Mach region. To prevent the advection flux from causing spurious velocities when surface tension is present, the pressure-velocity coupling term is modified to ensure it vanishes at material interfaces. Then, we derive the pressure Helmholtz equation to solve the final pressure and update the intermediate states to the solution variables at the next time step. The proposed hybrid approach retains the upwind property of the AUSM scheme for high Mach numbers while recovering central schemes and the standard projection solution for low Mach limits. To accurately resolve the complex flow structures including shock waves and material interfaces without numerical oscillations, a newly proposed homogenous ROUND (Reconstruction Operator on Unified Normalised-variable Diagram) reconstruction strategy is employed in this work. By simulating high-speed compressible multiphase flows and incompressible multiphase flows, this study demonstrates the ability of the proposed method to accurately handle flow regimes across all Mach numbers.

physics.flu-dyn↗

arXiv2024

On the convection boundedness of numerical schemes across discontinuities

This short note introduces a novel diagnostic tool for evaluating the convection boundedness properties of numerical schemes across discontinuities. The proposed method is based on the convection boundedness criterion and the normalised variable diagram. By utilising this tool, we can determine the CFL conditions for numerical schemes to satisfy the convection boundedness criterion, identify the locations of over- and under-shoots, optimize the free parameters in the schemes, and develop strategies to prevent numerical oscillations across the discontinuity. We apply the diagnostic tool to assess representative discontinuity-capturing schemes, including THINC, fifth-order WENO, and fifth-order TENO, and validate the conclusions drawn through numerical tests. We further demonstrate the application of the proposed method by formulating a new THINC scheme with less stringent CFL conditions.

math.NA↗

arXiv2024

Neural Control Variates with Automatic Integration

This paper presents a method to leverage arbitrary neural network architecture for control variates. Control variates are crucial in reducing the variance of Monte Carlo integration, but they hinge on finding a function that both correlates with the integrand and has a known analytical integral. Traditional approaches rely on heuristics to choose this function, which might not be expressive enough to correlate well with the integrand. Recent research alleviates this issue by modeling the integrands with a learnable parametric model, such as a neural network. However, the challenge remains in creating an expressive parametric model with a known analytical integral. This paper proposes a novel approach to construct learnable parametric control variates functions from arbitrary neural network architectures. Instead of using a network to approximate the integrand directly, we employ the network to approximate the anti-derivative of the integrand. This allows us to use automatic differentiation to create a function whose integration can be constructed by the antiderivative network. We apply our method to solve partial differential equations using the Walk-on-sphere algorithm. Our results indicate that this approach is unbiased and uses various network architectures to achieve lower variance than other control variate methods.

cs.LG↗

arXiv2024

A Simple Approach to Differentiable Rendering of SDFs

We present a simple algorithm for differentiable rendering of surfaces represented by Signed Distance Fields (SDF), which makes it easy to integrate rendering into gradient-based optimization pipelines. To tackle visibility-related derivatives that make rendering non-differentiable, existing physically based differentiable rendering methods often rely on elaborate guiding data structures or reparameterization with a global impact on variance. In this article, we investigate an alternative that embraces nonzero bias in exchange for low variance and architectural simplicity. Our method expands the lower-dimensional boundary integral into a thin band that is easy to sample when the underlying surface is represented by an SDF. We demonstrate the performance and robustness of our formulation in end-to-end inverse rendering tasks, where it obtains results that are competitive with or superior to existing work.

cs.GR↗

arXiv2024

Rendering Participating Media Using Path Graphs

Rendering volumetric scattering media, including clouds, fog, smoke, and other complex materials, is crucial for realism in computer graphics. Traditional path tracing, while unbiased, requires many long path samples to converge in scenes with scattering media, and a lot of work is wasted by paths that make a negligible contribution to the image. Methods to make better use of the information learned during path tracing range from photon mapping to radiance caching, but struggle to support the full range of heterogeneous scattering media. This paper introduces a new volumetric rendering algorithm that extends and adapts the previous \emph{path graph} surface rendering algorithm. Our method leverages the information collected through multiple-scattering transport paths to compute lower-noise estimates, increasing computational efficiency by reducing the required sample count. Our key contributions include an extended path graph for participating media and new aggregation and propagation operators for efficient path reuse in volumes. Compared to previous methods, our approach significantly boosts convergence in scenes with challenging volumetric light transport, including heterogeneous media with high scattering albedos and dense, forward-scattering translucent materials, under complex lighting conditions.

cs.GR↗

arXiv2024

A low-dissipation reconstruction scheme for compressible single- and multi-phase flows based on artificial neural networks

Solving compressible flows containing both smooth and discontinuous flow structures remains a significant challenge for finite volume methods. Godunov-type finite volume methods are commonly used for numerical simulations of compressible flows. One of the key factors in obtaining high-quality solutions is high-fidelity spatial reconstruction. In this work, we introduce a new paradigm for constructing high-resolution hybrid reconstruction schemes for compressible flows. This approach generates training data based on BVD schemes for supervised learning and employs ANN to create an indicator that pre-selects the most suitable reconstruction scheme for each cell, achieving the lowest global numerical dissipation. The numerical schemes under this paradigm are more computationally efficient than similar schemes within the BVD framework, as each cell only requires constructing a single interpolation function. Following this paradigm, a novel low-dissipation reconstruction scheme based on the MUSCL-THINC-BVD scheme, named the deepMTBVD scheme, is proposed for compressible single- and multi-phase flows. The performance of the proposed scheme has been extensively verified through benchmark tests of single- and multi-phase compressible flows, where discontinuous and vortical flow structures, like shock waves, contact discontinuities, and material interfaces, as well as vortices and shear instabilities of different scales, coexist simultaneously. Numerical results indicate that the new deepMTBVD scheme performs as well as the original MUSCL-THINC-BVD scheme for numerical simulations of compressible flows while reducing computational time by up to 40%.

physics.flu-dyn↗

arXiv2023

Direct numerical simulation of inflow boundary-layer turbulence effects on cavity flame stabilisation in a model scramjet combustor

Supersonic lean premixed hydrogen/air combustion stabilised by a cavity-flame holder within a model scramjet, characterized by a Mach 1.5 inflow at 1000 K and 50 kPa, is investigated via direct numerical simulation. By separately implementing wall-bounded turbulent and laminar inlet conditions, this work analysis various physical processes of flame stabilization and turbulence-flame interactions to study the influence of inflow boundary layer conditions. Findings indicate that combustion occurred within the cavity shear layer in both cases and propagated downstream along the lower wall. Also, the server impingement at the rear wall in the case with laminar inflow leads to greater cavity resistance. Furthermore, the studies on gas exchange and transport process indicates that with laminar inflow the entered gas accumulates in the back part of the cavity via the intensive mass exchange process and weaker interaction between the primary and secondary vortices. Flame stretch and thickness are further investigated to shed light into turbulence-flame interaction in supersonic flows. Findings indicates that the case with inflow wall-bounded turbulence show similar behaviours compared to previous studies, whereas the observed phenomena in front part of cavity shear layer are differ due to the presence of roll-up vortices in the case with laminar inflow. Overall, the influence of tangential strain rate and curvature are consistent with the preceding results and the evolution of flame thickness is caused by the combined effect of the two factors in both cases.

physics.flu-dyn↗

arXiv2022

Robotic Dough Shaping

Robotic manipulation of deformable objects gains great attention due to its wide applications including medical surgery, home assistance, and automatic food preparation. The ability to deform soft objects remains a great challenge for robots due to difficulties in defining the problem mathematically. In this paper, we address the problem of shaping a piece of dough-like deformable material into a 2D target shape presented upfront. We use a 6 degree-of-freedom WidowX-250 Robot Arm equipped with a rolling pin and information collected from an RGB-D camera and a tactile sensor. We present and compare several control policies, including a dough shrinking action, in extensive experiments across three kinds of deformable materials and across three target dough shape sizes, achieving the intersection over union (IoU) of 0.90. Our results show that: i) rolling dough from the highest dough point is more efficient than from the 2D/3D dough centroid; ii) it might be better to stop the roll movement at the current dough boundary as opposed to the target shape outline; iii) the shrink action might be beneficial only if properly tuned with respect to the expand action; and iv) the Play-Doh material is easier to shape to a target shape as compared to Plasticine or Kinetic sand. Video demonstrations of our work are available at https://youtu.be/ZzLMxuITdt4

cs.RO↗

arXiv2022

A data management system for machine learning research of tokamak

In recent years, machine learning (ML) research methods have received increasing attention in the tokamak community. The conventional database (i.e., MDSplus for tokamak) of experimental data has been designed for small group consumption and is mainly aimed at simultaneous visualization of a small amount of data. The ML data access patterns fundamentally differ from traditional data access patterns. The typical MDSplus database is increasingly showing its limitations. We developed a new data management system suitable for tokamak machine learning research based on Experimental Advanced Superconducting Tokamak (EAST) data. The data management system is based on MongoDB and Hierarchical Data Format version 5 (HDF5). Currently, the entire data management has more than 3000 channels of data. The system can provide highly reliable concurrent access. The system includes error correction, MDSplus original data conversion, and high-performance sequence data output. Further, some valuable functions are implemented to accelerate ML model training of fusion, such as bucketing generator, the concatenating buffer, and distributed sequence generation. This data management system is more suitable for fusion machine learning model R\&D than MDSplus, but it can not replace the MDSplus database. The MDSplus database is still the backend for EAST tokamak data acquisition and storage.

physics.plasm-ph↗

arXiv2021

The Preliminary design of DC Magnet Power Supply System for ITER Static Magnetic Field Test facility

ITER (International Thermonuclear Experimental Reactor) static magnetic field (SMF) test facility requires a DC power supply with low voltage, high current, and high stability. Due to the limitation ofswitching loss, there is a contradiction between the output current capability and the output ripple. Large output current usually leads to low switching frequency, and low switching frequency will generate a large number of harmonics. To solve the problems, a topology based on the interleaving parallel buck converter is used and tested in this paper. Moreover, the topology is realized with only a small number of switching metal-oxide-semiconductor field effect transistors (MOSFETs). This article introduces the system design scheme and control method in detail. The analysis of harmonic and simulation are carried out. The validity of proposed scheme and control strategy were confirmed by experiments, the power supply system can supply large current of 15kA and has ability of low ripple.

physics.ins-det↗

arXiv2021

High-Uniformity Calculation Method of Four-Coil Configuration in Large-Caliber Magnetic Field Immunity Testing System

Power electronic equipment regulated by the International Thermonuclear Experimental Reactor (ITER) organization must pass the relevant steady-state magnetic field immunity test. The main body of magnetic field immunity test is magnetic field generator coil. Through mathematical derivation in this paper, the magnetic field calculation formulas of four-coil configuration under ideal and actual models are obtained. The traditional method of magnetic field performance calculation is compared with the general formula method under the ideal model. A global parameter optimization method based on Lagrange Multiplier by KKT conditions is proposed to obtain the coil parameters of high-uniformity magnetic field. The magnetic field distribution in the uniform zone is revealed by the finite element method. The model analysis is proved to be correct and effective by experimental results. The research of this paper provides a practical scheme for the coil design with high magnetic field and high-quality uniformity.

physics.ins-det↗

arXiv2021

A Comprehensive Evaluation of Android ICC Resolution Techniques

Inter-component communication (ICC) is a widely used mechanism in mobile apps, which enables message-based control flow transferring and data passing between Android components. Effective ICC resolution requires precisely identifying entry points, analyzing data values of ICC fields, modeling related framework APIs, etc. Due to various control-flow- and data-flow-related characteristics involved and the lack of oracles for real-world apps, the comprehensive evaluation of ICC resolution techniques is challenging. To fill this gap, we collect multiple-type benchmark suites with 4,104 apps, covering hand-made apps, open-source, and commercial ones. Considering their differences, various evaluation metrics, e.g., number count, graph structure, and reliable oracle based metrics, are adopted on-demand. As the oracle for real-world apps is unavailable, we design a dynamic analysis approach to extract the real ICC links triggered during GUI exploration. By auditing the code implementations, we carefully check the extracted ICCs and confirm 1,680 ones to form a reliable oracle set, in which each ICC is labeled with 25 code characteristic tags. The evaluation performed on six state-of-the-art ICC resolution tools shows that 1) the completeness of static ICC resolution results on real-world apps is not satisfactory, as up to 38%-85% ICCs are missed by tools; 2) many wrongly reported ICCs are sent from or received by only a few components and the graph structure information can help the identification; 3) the efficiency of fundamental tools, like ICC resolution ones, should be optimized in both engineering and research aspects. By investigating both the missed and wrongly reported ICCs, we discuss the strengths of different tools for users and summarize eight common FN/FP patterns in ICC resolution for tool developers.

cs.SE↗

arXiv2020

A new paradigm of dissipation-controllable, multi-scale resolving schemes for compressible flows

The scale-resolving simulation of high speed compressible flow through direct numerical simulation (DNS) or large eddy simulation (LES) requires shock-capturing schemes to be more accurate for resolving broadband turbulence and robust for capturing strong shock waves. In this work, we develop a new paradigm of dissipation-controllable, shock capturing scheme to resolve multi-scale flow structures in high speed compressible flow. This novel paradigm of shock-capturing scheme is named as PnTm-BVD-CD. The proposed PnTm-BVD-CD scheme has following desirable properties. First, it can capture large-scale discontinuous structures such as strong shock waves without obvious non-physical oscillations while resolving sharp contact, material interface and shear layer. Secondly, the numerical dissipation property of PnTm-BVD-CD can be effectively controlled between n+1 order upwind-biased scheme and non-dissipative n+2 order central scheme through a simple tunable parameter $\lambda$. Thirdly, with $\lambda=0.5$ the scheme can recover to n+2 order non-dissipative central interpolation for smooth solution over all wavenumber, which is preferable for solving small-scale structures in DNS as well as resolvable-scale in explicit LES. Finally, the under-resolved small-scale can be solved with dissipation controllable algorithm through so-called implicit LES (ILES) approach.

physics.comp-ph↗

arXiv2020

Positivity-preserving hybrid DG/FV method for compressible Euler equations with stiff source terms

In numerical simulations of complex fluid dynamical problems, unphysical negative density or pressure may appear, causing blow-up of the computation. With the aim of obtaining positivity-preserving solutions with multi-scale resolution for solving strongly compressible flows, including hypersonic flows and stiff detonation waves, we present a positivity-preserving hybrid discontinuous Galerkin/finite volume (DG/FV) method. The approach is based on a priori and a posteriori computational methodology. The a priori computation utilizes relaxed boundary variation diminishing (BVD) algorithm to find troubled cells where the DG operators are replaced by the FV operators. The FV operators then deploy a hyperbolic tangent function in the reconstruction procedure to prevent unphysical values appearing in the flux evaluation. The a posteriori computation detects unphysical negative values in a simplified version of multidimensional optimal order detection (MOOD) paradigm and in the worst case applies the first-order FV Godunov scheme to guarantee the positivity of the solution. The a priori computation produces fewer oscillatory solutions, so that the a posteriori computation triggers less first-order evaluation. A technique of utilizing the tangent of hyperbola for interface capturing (THINC) with anti-diffusion effect, which is also referred to as the technique of adaptive reconstruction in this work, is suggested to reduce the numerical dissipation of the shock-capturing scheme. The current approaches retain the capability of the DG scheme to resolve small scales and the capability of the FV scheme to capture sharp discontinuities at the subgrid level.

physics.comp-ph↗