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Thermodynamic uncertainty relations yield a lower bound on entropy production in terms of the mean and fluctuations of a current. We derive their general form for systems under arbitrary time-dependent driving from arbitrary initial states…

Statistical Mechanics · Physics 2021-01-12 Timur Koyuk , Udo Seifert

As the sensitivity and uncertainty analysis of nuclear system can provide more confident bounds for the Best-estimate Prediction used to assess the performance and safety of nuclear plant, the uncertainty and sensitivity analysis has been a…

Applied Physics · Physics 2017-04-24 JiaYi Xu , Xu Bo Ma , Fan Lu , Yi Xue Chen

We extend a class of recently derived thermodynamic uncertainty relations to vector-valued observables. In contrast to the scalar-valued observables examined previously, this multidimensional thermodynamic uncertainty relation provides a…

Statistical Mechanics · Physics 2019-10-22 Andreas Dechant

Based on the idea of maintaining physical diffuse interface kinetics, enhancing interfacial diffusivity has recently provided a new direction for quantitative phase-field simulation at microstructural length and time scale. Establishing a…

Materials Science · Physics 2024-05-21 Yue Li , Lei Wang , Junjie Li , Jincheng Wang , Zhijun Wang

The complexity of experimental setups in the field of cyber-physical energy systems has motivated the development of the Holistic Test Description (HTD), a well-adopted approach for documenting and communicating test designs. Uncertainty,…

This guide offers suggestions/insights on uncertainty quantification of nuclear structure models. We discuss a simple approach to statistical error estimates, strategies to assess systematic errors, and show how to uncover…

Nuclear Theory · Physics 2014-05-26 J. Dobaczewski , W. Nazarewicz , P. -G. Reinhard

The present paper describes the development of a novel and comprehensive computational framework to simulate solidification problems in materials processing, specifically casting processes. Heat transfer, solidification and fluid flow due…

Numerical Analysis · Computer Science 2020-10-06 Shantanu Shahane , Narayana Aluru , Placid Ferreira , Shiv G Kapoor , Surya Pratap Vanka

A non-isothermal phase field model that captures both displacive and diffusive phase transformations in a unified framework is presented. The model is developed in a formal thermodynamic setting, which provides guidance on admissible…

Materials Science · Physics 2011-12-02 Mirko Maraldi , Garth N. Wells , Luisa Molari

A new method for identifying crystalline phases in X-ray diffraction data has been proposed, which is especially useful for the study of multiphase materials (more than eight - ten phases) with a relatively low content (less than 1 - 3…

Materials Science · Physics 2021-08-17 A. D. Skorbun , S. V. Gabielkov , I. V. Zhyganiuk

A framework is developed based on different uncertainty quantification (UQ) techniques in order to assess validation and verification (V&V) metrics in computational physics problems, in general, and computational fluid dynamics (CFD), in…

Computational Physics · Physics 2020-07-15 Saleh Rezaeiravesh , Ricardo Vinuesa , Philipp Schlatter

The thermodynamic uncertainty relation is an inequality stating that it is impossible to attain higher precision than the bound defined by entropy production. In statistical inference theory, information inequalities assert that it is…

Statistical Mechanics · Physics 2019-06-26 Yoshihiko Hasegawa , Tan Van Vu

Equilibrium molecular dynamics (EMD) simulations along with the Green-Kubo formula have been widely used to calculate lattice thermal conductivities. Previous studies, however, focused primarily on the calculated thermal conductivities,…

Materials Science · Physics 2016-12-21 Zuyuan Wang , Salar Safarkhani , Guang Lin , Xiulin Ruan

ML models have errors when used for predictions. The errors are unknown but can be quantified by model uncertainty. When multiple ML models are trained using the same training points, their model uncertainties may be statistically…

Machine Learning · Statistics 2025-09-23 Xiaoping Du

Constitutive model discovery refers to the task of identifying an appropriate model structure, usually from a predefined model library, while simultaneously inferring its material parameters. The data used for model discovery are measured…

Machine Learning · Computer Science 2026-01-27 David Anton , Henning Wessels , Ulrich Römer , Alexander Henkes , Jorge-Humberto Urrea-Quintero

Batteries are nonlinear dynamical systems that can be modeled by Porous Electrode Theory models. The aim of optimal fast charging is to reduce the charging time while keeping battery degradation low. Most past studies assume that model…

Systems and Control · Electrical Eng. & Systems 2024-10-14 Minsu Kim , Joachim Schaeffer , Marc D. Berliner , Berta Pedret Sagnier , Rolf Findeisen , Richard D. Braatz

Stochastic thermodynamics is formulated under the assumption of perfect knowledge of all thermodynamic parameters. However, in any real-world experiment, there is non-zero uncertainty about the precise value of temperatures, chemical…

Statistical Mechanics · Physics 2024-01-11 Jan Korbel , David H. Wolpert

Skew product systems with monotone one-dimensional fibre maps driven by piecewise expanding Markov interval maps may show the phenomenon of intermingled basins. To quantify the degree of intermingledness the uncertainty exponent and the…

Dynamical Systems · Mathematics 2018-03-01 Gerhard Keller

Classical density-functional theory is employed to study finite-temperature trends in the relative stabilities of one-component quasicrystals interacting via effective metallic pair potentials derived from pseudopotential theory. Comparing…

Materials Science · Physics 2009-10-30 A. R. Denton , J. Hafner

It is shown that the enthalpy of any close packed structure for a given element can be characterised as a linear expansion in a set of continuous variables $\alpha_n$ which describe the stacking configuration. This enables us to represent…

Materials Science · Physics 2017-11-22 Christian H. Loach , Graeme J. Ackland

Thermodynamics is a science concerning the state of a system, whether it is stable, metastable, or unstable. The combined law of thermodynamics derived by Gibbs about 150 years ago laid the foundation of thermodynamics. In Gibbs combined…

Statistical Mechanics · Physics 2023-08-03 Zi-Kui Liu