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The physical sciences are replete with dynamical systems that require the resolution of a wide range of length and time scales. This presents significant computational challenges since direct numerical simulation requires discretization at…

Machine Learning · Computer Science 2025-11-11 Andrew F. Ilersich , Prasanth B. Nair

Linear fractional stable motion, denoted by $\{X_{H,\al}(t)\}_{t\in \R}$, is one of the most classical stable processes; it depends on two parameters $H\in (0,1)$ and $\al\in (0,2)$. The parameter $H$ characterizes the self-similarity…

Statistics Theory · Mathematics 2013-02-08 Antoine Ayache , Julien Hamonier

We propose a new material viscoelastic model and mathematical solution to simulate relaxation modulus and viscoelastic response. The model formula of relaxation modulus is extended from sigmoidal function considering nonlinear strain…

Materials Science · Physics 2016-03-03 Qinwu Xu , Bjorn Engquist

Understanding the biomechanics of the heart in health and disease plays an important role in the diagnosis and treatment of heart failure. The use of computational biomechanical models for therapy assessment is paving the way for…

The system design and algorithm development of mobile 3D printing robots need a realistic simulation. They require a mobile robot simulation platform to interoperate with a physics-based material simulation for handling interactions between…

Robotics · Computer Science 2021-10-12 Uljad Berdica , Yuewei Fu , Yuchen Liu , Emmanouil Angelidis , Chen Feng

Machine learning is rapidly making its path into natural sciences, including high-energy physics. We present the first study that infers, directly from experimental data, a functional form of fragmentation functions. The latter represent a…

High Energy Physics - Phenomenology · Physics 2025-01-14 Nour Makke , Sanjay Chawla

Modern biomedical studies frequently collect complex, high-dimensional physiological signals using wearables and sensors along with time-to-event outcomes, making efficient variable selection methods crucial for interpretation and improving…

Methodology · Statistics 2026-04-22 Yuanzhen Yue , Stella Self , Yichao Wu , Jiajia Zhang , Rahul Ghosal

We apply reinforcement learning (RL) to robotics tasks. One of the drawbacks of traditional RL algorithms has been their poor sample efficiency. One approach to improve the sample efficiency is model-based RL. In our model-based RL…

Machine Learning · Computer Science 2023-05-16 Adithya Ramesh , Balaraman Ravindran

Establishing appropriate mathematical models for complex systems in natural phenomena not only helps deepen our understanding of nature but can also be used for state estimation and prediction. However, the extreme complexity of natural…

Machine Learning · Computer Science 2024-03-27 Cheng Fang , Jinqiao Duan

We introduce an efficient discretization of a novel fractional-order adaptive exponential (FrAdEx) integrate-and-fire model, which is used to study the fractional-order dynamics of neuronal activities. The discretization is based on…

Biological Physics · Physics 2025-11-13 Alexandru Fikl , Aman Jhinga , Eva Kaslik , Argha Mondal

We present a novel method for characterizing the microstructure of a material from volumetric datasets such as 3D image data from computed tomography (CT). The method is based on a new statistical model for the distribution of voxel…

Materials Science · Physics 2021-01-06 Elise Otterlei Brenne , Vedrana Andersen Dahl , Peter Stanley Jørgensen

This paper presents a new iterative reconstruction method to provide high-resolution images of shear modulus and viscosity via the internal measurement of displacement fields in tissues. To solve the inverse problem, we compute the…

Analysis of PDEs · Mathematics 2017-02-10 Habib Ammari , Jin Keun Seo , Liangdong Zhou

In this work we present a generalised viscoelastic model using distributed-order derivatives. The model consists of two distributed-order elements (distributed springpots) connected in series, as in the Maxwell model. The new model…

Classical Physics · Physics 2022-12-28 Luis Ferrás , Maria Luisa Morgado , Magda Rebelo

Robotic fabric manipulation has applications in home robotics, textiles, senior care and surgery. Existing fabric manipulation techniques, however, are designed for specific tasks, making it difficult to generalize across different but…

Fracture of viscoelastic materials is considered to be a complex phenomenon due to their highly rate sensitive behavior. In this context, we are interested in the quasi-static response of a viscoelastic solid subjected to damage. This paper…

Computational Engineering, Finance, and Science · Computer Science 2023-05-15 Rajasekar Gopalsamy , Nicolas Chevaugeon , Olivier Chupin , Ferhat Hammoum

Fractal patterns are observed in computational mechanics of elastic-plastic transitions in two models of linear elastic/perfectly-plastic random heterogeneous materials: (1) a composite made of locally isotropic grains with weak random…

Statistical Mechanics · Physics 2015-05-13 J. Li , M. Ostoja-Starzewski

Accurate identification of nonlinear material parameters from three-dimensional full-field deformation data remains a challenge in experimental mechanics. The virtual fields method (VFM) provides a powerful, computationally efficient…

Soft Condensed Matter · Physics 2026-01-21 Denislav P. Nikolov , Zhiren Zhu , Jonathan B. Estrada

We present and review several models of fractional viscous stresses from the literature, which generalise classical viscosity theories to fractional orders by replacing total strain derivatives in time with fractional time derivatives. We…

Soft Condensed Matter · Physics 2024-03-14 Harold Berjamin , Michel Destrade

Virtual garment simulation has become increasingly important with applications in garment design and virtual try-on. However, reproducing garments faithfully remains a cumbersome process. We propose an end-to-end method for estimating…

Graphics · Computer Science 2024-01-30 Joy Xiaoji Zhang , Gene Wei-Chin Lin , Lukas Bode , Hsiao-yu Chen , Tuur Stuyck , Egor Larionov

Sequential experimental design to discover interventions that achieve a desired outcome is a key problem in various domains including science, engineering and public policy. When the space of possible interventions is large, making an…

Machine Learning · Computer Science 2023-08-17 Jiaqi Zhang , Louis Cammarata , Chandler Squires , Themistoklis P. Sapsis , Caroline Uhler
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