Institution profile

Norwegian University of Science and Technology

Academic institutioneurope · no
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Research library404linked papers
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Selected work

Representative Papers

Artificial intelligence in materials science and engineering: Current landscape, key challenges, and future trajectories

Jul 01, 2025Composite structures

Materials development faces significant challenges including data complexity, lengthy timelines, and low efficiency, necessitating intelligent approaches to accelerate discovery. This work provides a systematic review of artificial intelligence applications in materials science, integrating a spectrum of techniques from traditional machine learning to deep learning and generative AI. It focuses on representation methods and model construction for multimodal data—such as composition, structure, images, and text—and covers core algorithms including convolutional neural networks (CNNs), graph neural networks (GNNs), Transformers, and Gaussian processes. The review particularly highlights emerging directions such as uncertainty quantification, multi-source data fusion, and language-inspired representations, proposing a research pathway toward intelligent materials design. By offering a comprehensive AI framework, this study identifies critical challenges in data quality, standardization, and algorithmic adaptability, thereby significantly enhancing the efficiency and reliability of materials discovery and optimization.

10 citationsRead paper

First-order friction models with bristle dynamics: lumped and distributed formulations

Feb 10, 2026

This work addresses the limitations of existing rate-dependent friction models, which are often empirical, lack physical interpretability, and fail to satisfy mathematical properties essential for control and estimation. By leveraging fundamental physical principles and inverting the dynamics of bristle elements, the authors propose a physically grounded first-order dynamic friction modeling framework that guarantees stability and passivity. The framework not only recovers lumped-parameter models akin to LuGre but also, for the first time, yields a distributed-parameter hyperbolic partial differential equation (PDE) model directly linked to bristle dynamics, suitable for rolling contact scenarios. Experimental validation demonstrates that the proposed model reproduces key behaviors of the LuGre model while revealing critical differences, thereby exhibiting superior physical consistency and modeling efficacy.

7 citationsRead paper

elaTCSF: A Temporal Contrast Sensitivity Function for Flicker Detection and Modeling Variable Refresh Rate Flicker

Dec 03, 2024ACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia

This paper addresses the insufficient modeling of low-contrast flicker perception in display and lighting applications—particularly under variable refresh rate (VRR), peripheral vision, and low-spatial-frequency conditions where existing models fail. We propose the first extended temporal contrast sensitivity function (elaTCSF), jointly incorporating luminance, retinal eccentricity, and stimulus area. Methodologically, we systematically integrate these three factors into the TCSF framework, develop a spatial probability summation model, and introduce the first VRR-specific flicker detection benchmark dataset. Key contributions include: resolving the long-standing controversy regarding enhanced flicker visibility in peripheral vision; enabling high-accuracy prediction of low-persistence flicker in VR; empirically characterizing the flicker-free operational range for VRR systems; and supporting high-fidelity display and lighting design. Our elaTCSF consistently outperforms IDMS-TCSF across multiple benchmark datasets.

2 citationsRead paper

Generalized Single-Image-Based Morphing Attack Detection Using Deep Representations from Vision Transformer

Jun 17, 20242024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Detecting single-image face forgery in open-set scenarios—where unknown generative algorithms, post-processing techniques, and acquisition devices severely degrade generalization—remains challenging. Method: This paper introduces Vision Transformers (ViTs) to this task for the first time, leveraging their global-local joint modeling capability to precisely capture sparse, fine-grained fusion artifacts across the entire face. We propose an end-to-end supervised training framework and adopt a rigorous cross-dataset evaluation protocol to assess generalization. Results: Our method significantly outperforms CNN-based baselines under cross-dataset testing and achieves state-of-the-art performance on intra-dataset benchmarks. This work establishes a robust, scalable liveness detection paradigm for high-security applications such as border control and identity document verification.

2 citationsRead paper

Semilinear single-track vehicle models with distributed tyre friction dynamics

Jan 01, 2026Nonlinear dynamics

This study addresses the limitations of conventional single-track vehicle models in accurately capturing the coupled effects of frictional nonlinearity and transient tire deformation. To overcome this, a novel semi-linear single-track model is proposed, integrating distributed friction brush dynamics (FrBD) to embed transient friction effects from rolling contact into the full-vehicle dynamics as a semi-linear partial differential equation (PDE). This formulation yields an ODE–PDE coupled system that unifies classical friction models such as Dahl and LuGre within a single framework for the first time. The well-posedness and physical consistency of the system are rigorously established, accommodating both flexible and rigid tire carcass configurations. Simulations successfully reproduce micro-oscillations and complex transient lateral responses under aggressive steering maneuvers, demonstrating the model’s superior balance of physical fidelity, mathematical rigor, and computational tractability.

1 citationsRead paper
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