180 results
Filters
Settings
Now showing1 - 10 of 180
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Inelastic Constitutive Kolmogorov–Arnold Networks: a generalized framework for automated discovery of interpretable inelastic material models(Elsevier, 2026-11-01); ; ; ; A key problem of solid mechanics is the identification of the constitutive law of a material, that is, the relation between strain history and stress. Machine learning has led to considerable advances in this field lately. Here we introduce inelastic Constitutive Kolmogorov–Arnold Networks (iCKANs). This novel artificial neural network architecture can discover in an automated manner symbolic constitutive laws describing both the elastic and inelastic behavior of materials. That is, it can translate data from material testing into corresponding free energy and dissipation potential functions in closed mathematical form. We demonstrate the advantages of iCKANs using both synthetic data and experimental data of the viscoelastic polymer materials VHB 4910 and VHB 4905. The results demonstrate that iCKANs accurately capture complex viscoelastic behavior while preserving physical interpretability. It is a particular strength of iCKANs that they can process not only mechanical data but also arbitrary additional information available about a material (e.g., about temperature-dependent behavior). This makes iCKANs a powerful tool to discover in the future also how specific processing or service conditions affect the properties of materials.Publicationtype: Journal Article20 7 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Modeling anisotropic atomic vibrations in diffusive molecular dynamics of magnesium hydride(2026-10-23); The coupled diffusion and deformation characterizing diffusive processes on the nanoscale provide significant challenges to many simulation methods due to the combination of fast atomic vibrations and slow diffusion. Diffusive molecular dynamics (DMD) models such processes by a scale separation modeling the thermal vibrations as a spatial position probability distribution and diffusion as atom sites with variable concentrations, which evolve based on kinetic equations. This study introduces a novel type of fully anisotropic spatial probability distribution to improve the approximations for atom sites with anisotropic potential energy wells, e.g. close to surfaces or lattice defects and atoms in lattices with a relatively high degree of intrinsic anisotropy. A comparative study between the classic isotropic and newly introduced anisotropic probability distributions is conducted for the case of magnesium hydride with molecular dynamics simulations using the same interatomic potential serving as reference solution. Tests range from lattice and elastic constants of perfect crystals to more complex cases like the bending of nanobeams and reveal previously unreported shortcomings of isotropic DMD, leading to a loss of lattice symmetry and nonphysical behavior. The results obtained using anisotropic shape functions avoid this behavior and follow the reference solution significantly better while maintaining comparable computational effort. This confirms the requirement for DMD to take anisotropic vibrations into account when simulating magnesium hydride and other systems with anisotropic atom environments.Publicationtype: Journal Article6 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , LLM-driven design of physics-constrained constitutive models: Two agents are better than one(Springer, 2026-10-01); ; ; ; Developing constitutive models that capture how materials deform under load traditionally requires years of specialized expertise in continuum mechanics, machine learning, and scientific programming. Large language models (LLMs) have recently been shown to lower this barrier by generating constitutive models on demand, but existing single-agent pipelines lack systematic checks that the resulting models respect fundamental physical laws. To close this gap, we introduce the first multi-agent LLM-driven approach for constitutive model generation: a Creator agent proposes a model tailored to the data, while an Inspector agent critically audits each proposal against nine physical constraints and returns it for refinement whenever a violation is detected. We demonstrate this concept with constitutive artificial neural networks (CANNs) and benchmark it on brain tissue and rubber as isotropic materials, and on porcine skin tissue as a transversely isotropic material with a preferred fiber direction, using two different LLM backbones (Claude Opus 4.7 and Kimi K2.5). Whether a generated model satisfies the physical constraints is assessed numerically, by probing each constraint across a broad sample of deformation states, rotations, and perturbation directions. Adding the Inspector raises the share of exported models that pass all these checks from 90% to 95% for Opus and from 47% to 60% for Kimi. In addition, the generated models are on par with or even surpass expert-designed models in accuracy, extrapolate reliably beyond the training data, and generalize remarkably well to unseen loading paths. Separating generation from inspection thus turns LLM-driven constitutive modeling into a substantially more trustworthy process. The paradigm is deliberately technique-agnostic and scales automatically with advances in LLM capability, opening a promising path toward automated, physics-aware model discovery.Publicationtype: Journal Article7 25 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Different alloy, different story? Impact of transfer learning on predictive models for in silico screening of Mg dissolution modulators(Elsevier, 2026-09-26); ; ; ; Ideally, datasets for training machine learning models consist of thousands of versatile data samples — a scale that is seldom met in materials research to date. Transfer learning (TL) is widely used to mitigate data scarcity in available training sets by leveraging knowledge from related domains, since models pretrained on auxiliary datasets may learn structure–property relationships that are transferable from one to another. In this work, the potential of TL for predicting the performance of small organic molecule dissolution modulators is probed using corrosion inhibition responses of eleven Mg-based materials taken from the previously published ExCorr database. A significant improvement in prediction accuracy is observed when transfer learning is applied. However, for the dataset configuration studied here, the apparent cross-alloy TL gains are shown not to arise from transferable structure–activity relationship (SAR) knowledge but rather from a learned alloy-context signal embedded in the data. Seven Mg-based materials—six commercial alloys plus the high-purity variant HPMg51—form a shared-SAR cluster with strongly correlated inhibitor rankings, such that molecules labelled on one material are readily predictable for others within the cluster. Within this shared-SAR cluster, extensive overlap reduces the effective training pool from approximately 900 records to only around 180 unique molecules, placing the task firmly in a small-data regime. In this setting, an in-context large language model (Gemini 3.1 Pro) is the only approach that successfully learns meaningful SAR, with its pretrained knowledge providing the transfer that the explicit source dataset fails to deliver. Seven Mg-based materials in the ExCorr database form a shared-structure–activity relationship (SAR) cluster (pairwise Spearman r=0.67–0.92). Under molecule-disjoint filtering the cross-alloy TL gain on ExCorr splits into a null inhibitor-SAR contribution and a dominant alloy-context contribution. Cross-alloy overlap shrinks ExCorr’s training pool from ≈900records to ≈180unique molecules, only an in-context LLM (Gemini 3.1 Pro) stays predictive.Publicationtype: Journal Article6 2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , MV-GEL: Language-driven multi-view geometric entity localization on meshesIdentifying and grounding precise geometric entities, such as edges, planar regions, and curved surfaces within 3D objects, is foundational to computer-aided design (CAD), robotic manipulation, and scientific simulation. Although modern Vision Language Models (VLMs) have advanced referring segmentation (RIS) in the image domain, extending such language-driven localization to structured 3D geometry is substantially harder. The 3D object appearance is highly sensitive to viewpoints; a single perspective may render a target entity clearly observable, while another may suffer from severe occlusion or foreshortening. In this work, we attempt to solve these challenges with MV-GEL (Multi-View Geometric Entity Localization), a framework for localizing fine-grained geometric entities on polygon meshes from natural language queries. Our key insight is that reliable CAD entity (i.e., faces, edges or solids) localization depends on selecting views that make the queried entity maximally interpretable. We introduce GELviews, a prompt-conditioned ranking module that prioritizes viewpoints based on language prompted observability of geometric CAD entities. Selected views are processed by a VLM-based reasoning segmentation backbone, and predicted masks are lifted to the corresponding meshes via geometry-aware ray casting. Our framework is completely CAD agnostic and relies only on 3D meshes. Experiments show up to a 1.7× improvement in face-level IoU and over 4.5× gains in edge-level F1 compared to vanilla baselines, substantially outperforming CLIP-based and random view sampling, particularly for thin and view-sensitive structures. The dataset, code and trained checkpoints are available at https://github.com/kbali1297/MV-GEL.Publicationtype: Conference Paper12 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Dirichlet-type absorbing boundary conditions for ordinary state-based peridynamicsWe present time-domain Dirichlet-type absorbing boundary conditions (ABCs) for 2D ordinary state-based peridynamics using the Linear Peridynamic Solid (LPS) model, aimed at efficiently truncating unbounded elastodynamic domains while retaining the flexibility of arbitrary Poisson ratios. The proposed ABCs are built from a semi-analytical far-field representation expressed as a finite superposition of plane-wave modes satisfying the discrete LPS dispersion relation, so that outgoing waves are absorbed consistently with the numerical P- and S-wave branches and their Poisson-ratio-dependent polarizations. A cloud-based collocation and least-squares procedure eliminates the unknown modal amplitudes locally and yields precomputable updating operators that prescribe boundary displacement and velocity directly within explicit time stepping, avoiding spatial derivatives, transform techniques, auxiliary evolution equations, and surface-correction procedures at truncated horizons. Unlike our earlier bond-based ABC formulations, the present work requires a new state based far-field theory, a new discrete dispersion operator with coupled P- and S-wave branches for general Poisson ratios, and an additional treatment of the nonlocal dilatation field. Numerical benchmarks show that the resulting ABCs strongly suppress nonphysical reflections over a broad range of material parameters, providing a simple and robust alternative to absorbing layers.Publicationtype: Journal Article20 17 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Surface and boundary corrections in peridynamics using optimized nodal influence weights(Wiley, 2026-07-14); ; ; ; Peridynamics (PD), similarly to some other nonlocal continuum theories, exhibits truncated interaction horizons near free surfaces, cracks, and voids in bounded domains. This loss of neighbors causes artificial surface effects and inconsistencies in the derivative and energy operators that persist under discretization refinement, limiting PD accuracy and robustness. First, the analysis and numerical isolation of the effects of horizon truncation on these operators are carried out, showing how it induces surface softening in bond‐based and stiffening/softening in state‐based PD formulations. Then, a purely PD‐based surface correction is proposed to restore full‐horizon behavior while preserving the original horizon radius and bond topology. Each node is assigned a scalar influence weight, and every bond is scaled by the average of the endpoint weights to maintain symmetry. The nodal influence weights are computed in a preprocessing optimization step enforcing agreement between truncated‐ and full‐horizon derivative and energy operators. Unlike existing approaches, the proposed method does not rely on ghost particles, variable horizons, or reference solutions from classical continuum mechanics (CCM). Benchmarks in one, two, and three dimensions, including a dynamic brittle fracture test, show that the optimized nodal influence weights reduce boundary‐induced surface effects, improve energy consistency, and accelerate convergence of PD solutions. Analytical evaluation of the calibration targets over full‐horizons also improves interior volume integration accuracy. The proposed scheme is simple to implement in existing PD codes, incurs only a modest preprocessing cost, performs robustly for flat and curved boundaries on both regular and irregular point sets, produces reliable crack paths, and applies to both bond‐based and ordinary state‐based PD formulations. An open‐source Python implementation, perifit, is provided.Publicationtype: Journal Article41 61 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Comparative analysis of diffusive molecular dynamics and traditional molecular dynamics in palladium hydrideDiffusive processes on the nanoscale, characterized by coupled diffusion and deformation, pose significant challenges for the assessment using conventional simulation methods due to their long time scales. Diffusive molecular dynamics (DMD) models such diffusive processes by optimizing positions and vibration frequencies of atom sites with variable concentrations evolving based on kinetic equations. Despite its various applications, validation studies for this method still remain limited due to a lack of suitable reference solutions and an absence of comparisons among different kinetic equations. This paper aims to fill these gaps by directly comparing DMD, utilizing different kinetic equations, with traditional molecular dynamics (MD) for the case of palladium hydride on thermal and diffusive time scales. Employing the same interatomic potential in both methods helps to isolate errors attributed solely to the assumptions inherent in DMD. The simulations conducted, which include measurements of lattice and elastic constants as well as surface diffusion and hydration processes of a nanocube, reveal that although DMD generally captures the trends observed in the reference solutions quite well, it exhibits non-negligible quantitative discrepancies in both static properties and diffusive behaviors. Among the tested kinetic equations, a linear approach and a master equation depending on vacancy formation energy showed the best agreement with the reference solutions. This study underscores the necessity for further refinement of DMD like enhanced kinetic equations that account for local environmental factors and improved mean-field approximations better suited to complex interatomic potentials. Given the significant advantages DMD offers as a method, efforts in this direction are doubtlessly an interesting avenue for future research.Publicationtype: Journal Article23 6 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , LLMs as designers of physics-constrained neural networks for constitutive modeling: a demonstration on hyperelastic solids(Springer, 2026-06-12); ; ; ; Large language model (LLM)-based frameworks extend beyond agents; they also enable the on-demand creation of specialized scientific and engineering tools. We demonstrate this concept in the field of solid mechanics. There, so-called constitutive models describe the relationship between body deformation and mechanical stress. They are essential for both the scientific understanding and industrial application of materials. However, even recent data-driven methods of constitutive modeling, such as constitutive artificial neural networks (CANNs), still require substantial expert knowledge and human labor. We present a framework in which an LLM generates a CANN on demand, tailored to a given material class and dataset provided by the user. The framework covers LLM-based architecture selection, integration of physical constraints, and complete implementation. Evaluation on three benchmark problems demonstrates that LLM-generated CANNs achieve accuracy and generalization comparable to or greater than manually engineered counterparts, while substantially reducing the expertise required for constitutive modeling.Publicationtype: Journal Article33 24 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Concurrently coupling particle and continuum simulations to study block copolymer membrane fabrication(2026-06-02); ; ; ; We present a concurrent multiscale simulation framework for membrane fabrication from a block copolymer solution via self-assembly and nonsolvent-induced phase separation, combining simulations of a soft, coarse-grained particle model with the continuum Uneyama-Doi model. The computationally intensive particle model provides a molecularly resolved description of micro- and macrophase separation, including thermal fluctuations, while the computationally efficient continuum model captures the process-driven self-assembly of nonequilibrium membrane morphologies on large length and time scales. Central to the approach is a machine learning-guided adaptive coupling strategy, implemented through a coordinator library, which predicts the evolving spatiotemporal subdomain where high-fidelity particle simulations are required and dynamically allocates computational resources accordingly. The two complementary models are concurrently coupled through a consistent exchange of the solvents' fluxes, enabling the treatment of spatially inhomogeneous, multicomponent systems with diffusive transport, micro- and macrophase separation, and vitrification. This adaptive strategy enables predictive simulations of membrane formation on experimentally and technologically relevant length and time scales, reaching micrometers and minutes. As an application, we examine the influence of polymer concentration in the initial casting solution. The framework is general and extensible, providing a computational tool for investigating nonequilibrium structure formation in complex multicomponent soft-matter systems.Publicationtype: Journal Article17