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A computationally method on damage detection problems in structures was conducted using neural networks. The problem that is considered in this works consists of estimating the existence, location and extent of stiffness reduction in…

Neural and Evolutionary Computing · Computer Science 2008-07-01 Ismoyo Haryanto , Joga Dharma Setiawan , Agus Budiyono

Nanoindentation involves probing a hard diamond tip into a material, where the load and the displacement experienced by the tip is recorded continuously. This load-displacement data is a direct function of material's innate stress-strain…

Computational Engineering, Finance, and Science · Computer Science 2016-12-13 Salah U. Hamim

Atom probe tomography (APT) is ideally suited to characterize and understand the interplay of chemical segregation and microstructure in modern multicomponent materials. Yet, the quantitative analysis typically relies on human expertise to…

Resonant X-ray scattering (RXS) is a spectroscopy where both the power of site selective diffraction and the power of local absorption spectroscopy regarding atomic species are combined. By virtue of the dependence on the core level state…

Materials Science · Physics 2007-11-12 Yves Joly , Elena S. Nazarenko , José-Emilio Lorenzo , Sergio Di Matteo , Calogero R. Natoli

We introduce ultrafast low-energy electron diffraction (ULEED) in backscattering for the study of structural dynamics at surfaces. Using a tip-based source of ultrashort electron pulses, we investigate the optically-driven transition…

Mesoscale and Nanoscale Physics · Physics 2020-08-26 S. Vogelgesang , G. Storeck , S. Schramm , K. Rossnagel , S. Schäfer , C. Ropers

Disordered systems like liquids, gels, glasses, or granular materials are not only ubiquitous in daily life and in industrial applications but they are also crucial for the mechanical stability of cells or the transport of chemical and…

Disordered Systems and Neural Networks · Physics 2019-04-30 Zhen Zhang , Walter Kob

Synthesizing realistic microstructure images conditioned on processing parameters is crucial for understanding process-structure relationships in materials design. However, this task remains challenging due to limited training micrographs…

Materials Science · Physics 2025-11-21 Hoang Cuong Phan , Minh Tien Tran , Chihun Lee , Hoheok Kim , Sehyeok Oh , Dong-Kyu Kim , Ho Won Lee

In this study, we address causal inference when only observational data and a valid causal ordering from the causal graph are available. We introduce a set of flow models that can recover component-wise, invertible transformation of…

Machine Learning · Computer Science 2024-12-16 Minh Khoa Le , Kien Do , Truyen Tran

We introduce and test a general machine-learning-based technique for the inference of short term causal dependence between state variables of an unknown dynamical system from time series measurements of its state variables. Our technique…

Adaptation and Self-Organizing Systems · Physics 2020-12-18 Amitava Banerjee , Jaideep Pathak , Rajarshi Roy , Juan G. Restrepo , Edward Ott

Phase diagrams exhibiting extended solid-solution and lens-like melting are often reproduced using ideal solutions, where ideal mixing considers a fully random configurational entropy of mixing. In the field of irreversible thermodynamics,…

Materials Science · Physics 2024-02-06 Jonathan Paras , Antoine Allanore

Diffraction methods are at the heart of structure determination of solids. While Bragg-like scattering (pure point diffraction) is a characteristic feature of crystals and quasicrystals, it is not straightforward to interpret continuous…

Mathematical Physics · Physics 2009-06-26 Michael Baake , Uwe Grimm

We propose a novel differentiable physics engine for system identification of complex spring-rod assemblies. Unlike black-box data-driven methods for learning the evolution of a dynamical system and its parameters, we modularize the design…

Robotics · Computer Science 2020-04-30 Kun Wang , Mridul Aanjaneya , Kostas Bekris

Fabrication of semiconductor heterostructures is now so precise that metrology has become a key challenge for progress in science and applications. It is now relatively straightforward to characterize classic III-V and group IV…

We address the fabrication of nano-architectures by impacting thin layers of amorphous Ge deposited on SiO$_{2}$ with a Ga$^{+}$ ion beam and investigate the structural and optical properties of the resulting patterns. By adjusting beam…

Breakthroughs in nanotechnology have enabled the large-scale fabrication of nanoparticles with varied compositions and structures. Yet, evaluating their electrical conductivities remains challenging due to high volume and individual…

Applied Physics · Physics 2025-10-22 Yun Huang , Kai Xu , Zexi Liang , Huaizhi Li , Wenjuan Zhu , Donglei Emma Fan

Modern computational organic chemistry is becoming increasingly data-driven. There remain a large number of important unsolved problems in this area such as product prediction given reactants, drug discovery, and metric-optimized molecule…

Biomolecules · Quantitative Biology 2026-02-10 Josh Payne , Mario Srouji , Dian Ang Yap , Vineet Kosaraju

Diffusion involving atom transport from one location to another governs many important processes and behaviors such as precipitation and phase nucleation. Local chemical complexity in compositionally complex alloys poses challenges for…

Disordered Systems and Neural Networks · Physics 2024-05-10 Bin Xing , Timothy J. Rupert , Xiaoqing Pan , Penghui Cao

Determination of crystal structures of nanocrystalline or amorphous compounds is a great challenge in solid states chemistry and physics. Pair distribution function (PDF) analysis of X-Ray or neutron total scattering data has proven to be a…

Materials Science · Physics 2025-07-14 Magnus Kløve , Sanna Sommer , Bo B. Iversen , Bjørk Hammer , Wilke Dononelli

Scanning transmission electron microscopy (STEM) plays a critical role in modern materials science, enabling direct imaging of atomic structures and their evolution under external interferences. However, interpreting time-resolved STEM data…

Computer Vision and Pattern Recognition · Computer Science 2025-08-15 Hao Wang , Hongkui Zheng , Kai He , Abolfazl Razi

The objective of this study is to evaluate whether the number of requisite training samples can be reduced with the use of various transfer learning models for predicting, for example, the chemical source terms of the data-driven…

Chemical Physics · Physics 2024-12-18 Ki Sung Jung , Tarek Echekki , Jacqueline H. Chen , Mohammad Khalil