Journal of Computational Physics最新文献

{"title":"High-order nonuniform time-stepping and MBP-preserving linear schemes for the time-fractional Allen–Cahn equation","authors":"Bingyin Zhang ,&nbsp;Hong Wang ,&nbsp;Hongfei Fu","doi":"10.1016/j.jcp.2026.114694","DOIUrl":"10.1016/j.jcp.2026.114694","url":null,"abstract":"<div><div>In this paper, we present a class of nonuniform time-stepping, high-order linear stabilized schemes that can preserve both the discrete energy stability and maximum-bound principle (MBP) for the time-fractional Allen–Cahn equation. To this end, we develop a new prediction strategy to obtain a second-order and MBP-preserving predicted solution, which is then used to handle the nonlinear potential explicitly. Additionally, we introduce an essential nonnegative auxiliary functional that enables the design of an appropriate stabilization term to dominate the predicted nonlinear potential, and thus to preserve the discrete MBP. Combining the newly developed prediction strategy and auxiliary functional, we propose two unconditionally energy-stable linear stabilized schemes, <em>L</em>1 and <em>L</em>2-1_<em>σ</em> schemes. We show that the <em>L</em>1 scheme unconditionally preserves the discrete MBP, whereas the <em>L</em>2-1_<em>σ</em> scheme requires a mild time-step restriction. Furthermore, we develop an improved <em>L</em>2-1_<em>σ</em> scheme with enhanced MBP preservation for large time steps, achieved through a novel unbalanced stabilization term that leverages the boundedness and monotonicity of the auxiliary functional. Representative numerical examples validate the accuracy, effectiveness, and physics-preserving of the proposed methods.</div></div>","PeriodicalId":352,"journal":{"name":"Journal of Computational Physics","volume":"551 ","pages":"Article 114694"},"PeriodicalIF":3.8,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146035680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A consistent and scalable framework suitable for boiling flows using the conservative diffuse interface method","authors":"Lorenz Weber ,&nbsp;Aritra Mukherjee ,&nbsp;Andreas G. Class ,&nbsp;Luca Brandt","doi":"10.1016/j.jcp.2026.114680","DOIUrl":"10.1016/j.jcp.2026.114680","url":null,"abstract":"<div><div>Interface-resolved simulations are essential for predicting and understanding boiling heat transfer phenomena. Such simulations generally come at a high computational cost, which continues to motivate the development of efficient frameworks. In recent years, conservative second-order phase field methods have gained popularity due to their efficient representation of phase interfaces. However, their potential for simulating complex boiling phenomena has not yet been explored. To address this gap, we develop a consistent and highly efficient framework suitable for simulating large-scale boiling flows. We derive a set of mixture equations to describe the two-phase flow. The mixture equations are coupled with the <em>accurate conservative diffuse interface</em> method [1] to capture the interface. We present additional terms in the momentum balance equation and demonstrate that the proposed momentum balance modifications are mandatory for accurately capturing phase-change-induced pressure jumps. To solve the set of equations, an alternative Fast Fourier Transform (FFT)-based pressure solution scheme is proposed. Additionally, a modified kinetic phase change model is utilized that does not involve calculating temperature gradients and avoids problem-dependent parameters. The framework is tested against a variety of benchmark simulations, both with and without phase change. Moreover, we achieve improved accuracy when simulating bubble dynamics without phase change at high density ratios. We show that the proposed FFT-based pressure solution scheme exhibits superior performance in calculating interfacial pressure jumps compared with a commonly used FFT solver. Regardless of phase change, more accurate startup behaviour is observed. In the presence of phase change, we are successful in removing interfacial pressure oscillations. Across all phase-change benchmark simulations, the new phase change model consistently provides reliable results. Finally, we successfully simulate the dynamics of bubbles in superheated liquid subjected to gravity and validate the results with experimental data.</div></div>","PeriodicalId":352,"journal":{"name":"Journal of Computational Physics","volume":"551 ","pages":"Article 114680"},"PeriodicalIF":3.8,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146035676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Curvilinear Lagrangian discontinuous Galerkin method for resistive magneto-hydrodynamics","authors":"Ruoyu Han ,&nbsp;Zijin Zhu ,&nbsp;Yibing Chen ,&nbsp;Guoxi Ni","doi":"10.1016/j.jcp.2026.114698","DOIUrl":"10.1016/j.jcp.2026.114698","url":null,"abstract":"<div><div>In this paper, we present a cell-centered Lagrangian discontinuous Galerkin (DG) method for solving resistive magneto-hydrodynamics (MHD). The equations are solved using an implicit-explicit (IMEX) method. The right-hand side of the equations is classified as a hydrodynamic contribution, which is solved explicitly, and a magnetodynamic contribution which is solved implicitly. The conservative variables are discretized using Taylor basis functions within the reference element, and for magnetic field discretization, we employ a locally divergence-free basis, and it is transformed to the reference element using the Piola transformation. Nodal velocities are determined through an approximate Riemann solver. Curvilinear mesh is achieved through basis function deformation in physical space. Numerical experiments demonstrate the accuracy and robustness of the method.</div></div>","PeriodicalId":352,"journal":{"name":"Journal of Computational Physics","volume":"551 ","pages":"Article 114698"},"PeriodicalIF":3.8,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146035677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physics-graph-informed neural networks (PGINNs): From local point-wise constraint to global nodal association","authors":"Feng Chen,&nbsp;Yiran Meng,&nbsp;Kegan Li,&nbsp;Chaoran Yang,&nbsp;Jincheng Dai","doi":"10.1016/j.jcp.2026.114685","DOIUrl":"10.1016/j.jcp.2026.114685","url":null,"abstract":"<div><div>Physics-Informed Neural Networks (PINNs) and their variants have gained widespread attention as scientific machine learning (SciML) methods for solving Partial Differential Equations (PDEs), but their computational efficiency and accuracy in low-dimensional problems are still lower than that of classical numerical methods, such as Finite Element Method (FEM). This limitation mainly stems from the generalization error of PINNs, whose point-wise loss functional ignores the interactions between neighboring collocation points. To address this challenge, we propose a new Physics-Graph-Informed Neural Networks (abbreviated as PGINNs) framework that combines deep learning with a conservation-consistent nodal network. Compared to traditional PINNs, PGINNs encode two fundamental forms of graph-topological physical information: (1) global node topology operator represented by the sparse incidence matrix, and (2) local edge feature matrix learned through deep neural networks (DNNs). By reconstructing the loss function as a system of algebraic equations learned by the nodal network representation, PGINNs effectively reduce the generalization error while retaining the mesh-free advantage of PINNs. Theoretical analysis shows that the generalization error convergence rate of PGINNs is improved by an order of magnitude compared with that of PINNs. Numerical experiments for complex boundaries and piecewise homogeneous media validate the high accuracy and computational efficiency of the method. This work combines the advantages of both classical numerical analysis and data-driven PDE solvers, establishing a new direction for high-fidelity SciML.</div></div>","PeriodicalId":352,"journal":{"name":"Journal of Computational Physics","volume":"551 ","pages":"Article 114685"},"PeriodicalIF":3.8,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146035781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Non -degenerate marginal-likelihood calibration with application to quantum characterization","authors":"Mohammad Motamed ,&nbsp;N. Anders Petersson","doi":"10.1016/j.jcp.2026.114655","DOIUrl":"10.1016/j.jcp.2026.114655","url":null,"abstract":"<div><div>We propose a marginal likelihood strategy within the Kennedy-O’Hagan (KOH) Bayesian framework, where a Gaussian process (GP) models the discrepancy between a physical system and its simulator. Our approach introduces a novel marginalized likelihood by integrating out the degenerate eigenspace of the covariance matrix, rather than approximating the original likelihood. Unlike approximation methods that compromise accuracy for computational efficiency, our method defines an exact likelihood—distinct from the original but preserving all relevant information. This formulation achieves computational efficiency and stability, even for large datasets where the covariance matrix nears degeneracy. Applied to the characterization of a superconducting quantum device at Lawrence Livermore National Laboratory, the approach enhances the predictive accuracy of the Lindblad master equations for modeling Ramsey measurement data by effectively quantifying uncertainties consistent with the quantum data.</div></div>","PeriodicalId":352,"journal":{"name":"Journal of Computational Physics","volume":"551 ","pages":"Article 114655"},"PeriodicalIF":3.8,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning non-separable Hamiltonian systems with pseudo-symplectic neural network","authors":"Xupeng Cheng ,&nbsp;Lijin Wang ,&nbsp;Yanzhao Cao ,&nbsp;Chen Chen","doi":"10.1016/j.jcp.2025.114630","DOIUrl":"10.1016/j.jcp.2025.114630","url":null,"abstract":"<div><div>Hamiltonian systems are fundamental and crucial in both classical and quantum mechanics. Extracting governing differential equations of Hamiltonian systems from observed data can enhance understanding complex systems and predict their dynamical behavior, compensating the limitations of first-principle-modeling. Recent research on data-based neural network detection of Hamiltonian systems has focused on structure-preserving learning that incorporates the intrinsic properties such as the symplecticity into the neural network architecture. However, these approaches either apply only to separable Hamiltonian systems, or to non-separable ones but require more complex computational tools such as dimension augmentation and implicit prediction. In this paper we propose a novel one layer explicit Pseudo-Symplectic Neural Network (PSNN) model for learning non-separable Hamiltonian systems. To enhance the accuracy of neural network simulation and compensate the loss of accuracy due to non-exact symplecticity, we design the learnable Padé-type activation function for neural network output. With shorter training period, smaller sample size and fewer network parameters, we demonstrate through numerical experiments that the proposed PSNN model with Padé-type activation is versatile in learning and forecasting a wide array of Hamiltonian systems, surpassing benchmark neural networks including the ODE-net, HNN, SympNets, NSSNN and Taylor-net, in terms of prediction accuracy, long-term stability and energy-preservation.</div></div>","PeriodicalId":352,"journal":{"name":"Journal of Computational Physics","volume":"550 ","pages":"Article 114630"},"PeriodicalIF":3.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145923631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simulating acoustically-actuated flows in complex microchannels using the volume penalization technique","authors":"Khemraj Gautam Kshetri ,&nbsp;Amneet Pal Singh Bhalla ,&nbsp;Nitesh Nama","doi":"10.1016/j.jcp.2025.114635","DOIUrl":"10.1016/j.jcp.2025.114635","url":null,"abstract":"<div><div>We present a volume penalization technique for simulating acoustically-actuated flows in geometrically complex microchannels. Using a perturbation approach, the nonlinear response of an acoustically-actuated compressible Newtonian fluid moving over obstacles or flowing in a geometrically complex domain is segregated into two sub-problems: a harmonic first-order problem and a time-averaged second-order problem, where the latter utilizes forcing terms and boundary conditions arising from the first-order solution. This segregation results in two distinct volume penalized systems of equations. The no-slip boundary condition at the fluid-solid interface is enforced by prescribing a zero structure velocity for the first-order problem, while spatially varying Stokes drift—which depends on the gradient of the first-order solution—is prescribed as the structure velocity for the second-order problem. The harmonic first-order system is solved via MUMPS direct solver, whereas the steady state second-order system is solved iteratively using a novel projection method-based preconditioner. The preconditioned iterative solver for the second-order system is demonstrated to be highly effective and scalable with respect to increasing penalty force and grid resolution, respectively. A novel contour integration technique to evaluate the acoustic radiation force on an immersed object is also proposed. This technique circumvents the use of velocity derivatives within the smeared region. The contour integral is specifically tailored to Cartesian grids. Through a series of test cases featuring representative microfluidic geometries, we demonstrate excellent agreement between the volume penalized and body-fitted grid solutions for the primary first- and second-order fields as well as for the acoustic radiation force that depends on the gradients of these fields. We also identify suitable penalty factors and interfacial smearing widths to accurately capture the first- and second-order solutions. These results provide empirical evidence of the efficacy of the volume penalization method for simulating acoustic streaming problems that have commonly been analyzed using body-fitted methods in the acoustofluidic literature.</div></div>","PeriodicalId":352,"journal":{"name":"Journal of Computational Physics","volume":"550 ","pages":"Article 114635"},"PeriodicalIF":3.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145974704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of the stability of an immersed elastic surface using the method of regularized Stokeslets","authors":"Dana Ferranti,&nbsp;Sarah D. Olson","doi":"10.1016/j.jcp.2025.114636","DOIUrl":"10.1016/j.jcp.2025.114636","url":null,"abstract":"<div><div>A linear stability analysis of an elastic surface immersed in a viscous fluid is presented. The coupled system is modeled using the method of regularized Stokeslets (MRS), a Lagrangian method for simulating fluid-structure interaction at low Reynolds number. The linearized system is solved in a doubly periodic domain in a 3D fluid. The eigenvalues determine the theoretical critical time step for numerical stability for a forward Euler time integration, which are then verified numerically across several regularization functions, elastic models, and parameter choices. New doubly periodic regularized Stokeslets are presented, allowing for comparison of the stability properties of different regularization functions. The stability results for a common regularization function are approximated by a power law relating the regularization parameter and the surface discretization for two different elastic models. This relationship is empirically shown to hold in the different setting of a finite surface in a bulk fluid.</div></div>","PeriodicalId":352,"journal":{"name":"Journal of Computational Physics","volume":"550 ","pages":"Article 114636"},"PeriodicalIF":3.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145974388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Domain decomposition of the modified Born series approach for large-scale wave propagation simulations","authors":"Swapnil Mache ,&nbsp;Ivo M. Vellekoop","doi":"10.1016/j.jcp.2025.114619","DOIUrl":"10.1016/j.jcp.2025.114619","url":null,"abstract":"<div><div>The modified Born series (MBS) is a fast and accurate method for simulating wave propagation in complex structures. In the current implementation of the MBS, the simulation size is limited by the working memory of a single computer or graphics processing unit (GPU). Here, we present a domain decomposition method that enhances the scalability of the MBS by distributing the computations over multiple GPUs, while maintaining its accuracy, memory efficiency, and guaranteed monotonic convergence. With this new method, the computations can be performed in parallel, and a larger simulation size is possible as it is no longer limited to the memory size of a single computer or GPU. We show how to decompose large problems over subdomains and demonstrate our approach by solving the Helmholtz problem for a complex structure of 3.28 · 10⁷ cubic wavelengths (320 × 320 × 320 wavelengths) in just 45 minutes with a dual-GPU simulation.</div></div>","PeriodicalId":352,"journal":{"name":"Journal of Computational Physics","volume":"550 ","pages":"Article 114619"},"PeriodicalIF":3.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145974702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An asymptotic preserving kinetic scheme for the M1 model of non-local thermal transport for two-dimensional structured and unstructured meshes","authors":"Jean-Luc Feugeas ,&nbsp;Julien Mathiaud ,&nbsp;Luc Mieussens ,&nbsp;Thomas Vigier","doi":"10.1016/j.jcp.2025.114618","DOIUrl":"10.1016/j.jcp.2025.114618","url":null,"abstract":"<div><div>The M1 moment model for electron transport is commonly used to describe non-local thermal transport effects in laser-plasma simulations. In this article, we propose a new asymptotic-preserving scheme based on the Unified Gas Kinetic Scheme (UGKS) for this model in two-dimensional space. This finite volume kinetic scheme follows the same approach as in our previous article [1] and relies on a moment closure, at the numerical scale, of the microscopic flux of UGKS. The method is developed for both structured and unstructured meshes, and several techniques are introduced to ensure accurate fluxes in the diffusion limit. A second-order extension is also proposed. Several test cases validate the different aspects of the scheme and demonstrate its efficiency in multiscale simulations. In particular, the results demonstrate that this method accurately captures non-local thermal effects.</div></div>","PeriodicalId":352,"journal":{"name":"Journal of Computational Physics","volume":"550 ","pages":"Article 114618"},"PeriodicalIF":3.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145974705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}