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Integration against a probability distribution given its unnormalized density is a central task in Bayesian inference and other fields. We introduce new methods for approximating such expectations with a small set of weighted samples --…

Machine Learning · Statistics 2026-05-15 Ayoub Belhadji , Daniel Sharp , Youssef M. Marzouk

Multistage models based on relativistic viscous hydrodynamics have proven successful in describing hadron measurements from relativistic nuclear collisions. These measurements are sensitive to the shear and the bulk viscosities of QCD and…

The fundamental problem of friction in the presence of macroscopic adhesion, as in soft bodies, is receiving interest from many experimentalists. Since the first fracture mechanics `purely brittle' model of Savkoor and Briggs, models have…

Soft Condensed Matter · Physics 2018-11-14 A. Papangelo , M. Ciavarella

Shock-physics numerical codes are essential tools for describing the short but extreme fragmentation stage of the hypervelocity impact process on asteroids. However, accurately representing complex interior structures, surfaces, and contact…

Earth and Planetary Astrophysics · Physics 2026-04-16 Xiaoran Yan , Patrick Michel , Ruichen Ni , Yifei Jiao , Junfeng Li

The rapid advancement of data science and artificial intelligence has affected physics in numerous ways, including the application of Bayesian inference, setting the stage for a revolution in research methodology. Our group has proposed…

Data Analysis, Statistics and Probability · Physics 2024-08-28 Shun Katakami , Shuhei Kashiwamura , Kenji Nagata , Masaichiro Mizumaki , Masato Okada

A method is presented for the registration and correlation of intrinsic property maps of materials, including data from nanoindentation hardness, Electron Back-Scattered Diffraction (EBSD), Electron Micro-Probe Analysis (EPMA). This highly…

Materials Science · Physics 2021-01-05 C. M. Magazzeni , H. M. Gardner , I. Howe , P. Gopon , J. C. Waite , D. Rugg , D. E. J. Armstrong , A. J. Wilkinson

In the course of animal development, the shape of tissue emerges in part from mechanical and biochemical interactions between cells. Measuring stress in tissue is essential for studying morphogenesis and its physical constraints.…

Quantitative Methods · Quantitative Biology 2013-01-21 S. Ishihara , K. Sugimura , S. J. Cox , I. Bonnet , Y. Bellaiche , F. Graner

Nanoindentation is vital for probing mechanical properties, yet traditional grid-based workflows are inefficient for targeting specific microstructural features. We present an automated nanoindentation framework that integrates machine…

Instrumentation and Detectors · Physics 2025-04-28 Vivek Chawla , Dayakar Penumadu , Sergei Kalinin

A novel Material Point Method (MPM) is introduced for addressing frictional contact problems. In contrast to the standard multi-velocity field approach, this method employs a penalty method to evaluate contact forces at the discretised…

Numerical Analysis · Mathematics 2024-03-21 Emmanouil G. Kakouris , Manolis N. Chatzis , Savvas P. Triantafyllou

Material Flow Analysis (MFA) is used to quantify and understand the life cycles of materials from production to end of use, which enables environmental, social and economic impacts and interventions. MFA is challenging as available data is…

The quantitative nanomechanical characterization of soft materials using the nanoindentation technique requires further improvements in the performances of instruments, including their force resolution in particular. A micro-machined…

Instrumentation and Detectors · Physics 2019-01-29 Zhi Li , Sai Gao , Uwe Brand , Karla Hiller , Nicole Wollschlaeger , Frank Pohlenz

A Bayesian approach is developed to analyze change points in multivariate time series and space-time data. The methodology is used to assess the impact of extended inundation on the ecosystem of the Gulf Plains bioregion in northern…

Methodology · Statistics 2013-06-21 Chris Strickland , Robert Burdett , Robert Denham , Robert Kohn , Kerrie Mengersen

Machine-learned interatomic potentials are revolutionising atomistic materials simulations by providing accurate and scalable predictions within the scope covered by the training data. However, generation of an accurate and robust training…

Materials Science · Physics 2025-07-30 Mariia Radova , Wojciech G. Stark , Connor S. Allen , Reinhard J. Maurer , Albert P. Bartók

Molecular adhesion plays a central role in many biological systems, yet existing methods to quantify adhesive strength often struggle to bridge the gap between single-molecule resolution and biologically relevant environments. Here, we…

Understanding the oscillating behaviors that govern organisms' internal biological processes requires interdisciplinary efforts combining both biological and computer experiments, as the latter can complement the former by simulating…

Applications · Statistics 2024-12-17 Youngdeok Hwang , Hang J. Kim , Won Chang , Christian Hong , Steven N. MacEachern

Mechanistic models of single-neuron dynamics have been extensively studied in computational neuroscience. However, identifying which models can quantitatively reproduce empirically measured data has been challenging. We propose to overcome…

Metal surfaces have long been known to reconstruct, significantly influencing their structural and catalytic properties. Many key mechanistic aspects of these subtle transformations remain poorly understood due to limitations of previous…

Materials Science · Physics 2023-08-15 Cameron J. Owen , Yu Xie , Anders Johansson , Lixin Sun , Boris Kozinsky

A novel phase field material point method is introduced for robust simulation of dynamic fracture in elastic media considering the most general case of anisotropic surface energy. Anisotropy is explicitly introduced through a properly…

Numerical Analysis · Mathematics 2019-09-04 Emmanouil Kakouris , Savvas P. Triantafyllou

Electron and scanning probe microscopy produce vast amounts of data in the form of images or hyperspectral data, such as EELS or 4D STEM, that contain information on a wide range of structural, physical, and chemical properties of…

Machine Learning · Computer Science 2023-06-09 Arpan Biswas , Maxim Ziatdinov , Sergei V. Kalinin

We present a Bayesian approach to identify optimal transformations that map model input points to low dimensional latent variables. The "projection" mapping consists of an orthonormal matrix that is considered a priori unknown and needs to…

Machine Learning · Statistics 2021-09-22 Panagiotis Tsilifis , Piyush Pandita , Sayan Ghosh , Valeria Andreoli , Thomas Vandeputte , Liping Wang
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