Results 101 to 110 of about 171,313 (268)
A Unifying Approach to Self‐Organizing Systems Interacting via Conservation Laws
Advanced Intelligent Discovery, EarlyView.The article develops a unified way to model and analyze self‐organizing systems whose interactions are constrained by conservation laws. It represents physical/biological/engineered networks as graphs and builds projection operators (from incidence/cycle structure) that enforce those constraints and decompose network variables into constrained versus ...
F. Barrows +7 more
wiley +1 more source
Harnessing Machine Learning to Understand and Design Disordered Solids
Advanced Intelligent Discovery, EarlyView.This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley +1 more source
THE THERMODYNAMIC ANALYSIS OF CONDITIONS OF DISSIPATIVE PHASES OCCURRENCE AT INTERACTION OF INTERMETALLIC COMPOUNDS MICROCRYSTALS [PDF]
Физико-химические аспекты изучения кластеров, наноструктур и наноматериалов, 2011 Conditions of occurrence during diffusion interaction between intermetallic compounds the phases which are absent on the phase equilibrium diagram of binary system is received by methods of nonequilibrium nonlinear thermodynamics.
V.V. Zhigunov, A.A. Alehina
doaj
A Review of the System-Intrinsic Nonequilibrium Thermodynamics in Extended Space (MNEQT) with Applications. [PDF]
Entropy (Basel), 2021 Gujrati PD.
europepmc +1 more source
Strain fluctuation in a non-isothermal solid [PDF]
, 2022 In this thesis we use nonequilibrium thermodynamics tools in order to investigate spontaneous vibration fluctuations of continuous, elastic, conductive and dissipative solids in and out of equilibrium states. In particular we want to advance with respect
Mentasti, Giorgio
core
Advanced Intelligent Discovery, EarlyView.Phonons‐informed machine‐learning predictive models are propitious for reproducing thermal effects in computational materials science studies. Machine learning (ML) methods have become powerful tools for predicting material properties with near first‐principles accuracy and vastly reduced computational cost.
Pol Benítez +4 more
wiley +1 more source
AIP AdvancesThe detailed definition of temperature has been the fundamental subject for the nonequilibrium thermodynamic theory essentially for information and quantum thermodynamic theories.
Hiroshi Kobayashi
doaj +1 more source
Advanced Intelligent Discovery, EarlyView.This article outlines how artificial intelligence could reshape the design of next‐generation transistors as traditional scaling reaches its limits. It discusses emerging roles of machine learning across materials selection, device modeling, and fabrication processes, and highlights hierarchical reinforcement learning as a promising framework for ...
Shoubhanik Nath +4 more
wiley +1 more source
Thermodynamics for non-equilibrium pattern formation
AIP Advances, 2011 The second entropy theory for non-equilibrium thermodynamics is used to show that the optimum structure or pattern of a time-dependent system corresponds to the maximum entropy. A formula for the total entropy of convective heat flow is derived.
Phil Attard
doaj +1 more source
On Gibbs Equilibrium and Hillert Nonequilibrium Thermodynamics [PDF]
During his time at Royal Institute of Technology (Kungliga Tekniska Hogskolan, KTH) in Sweden, the present author learned nonequilibrium thermodynamics from Mats Hillert.
Liu, Zi-Kui
core +1 more source