Results 151 to 160 of about 4,616 (278)

Invariant Measure and Universality of the 2D Yang–Mills Langevin Dynamic

Communications on Pure and Applied Mathematics, Volume 79, Issue 8, Page 1973-2102, August 2026.
ABSTRACT We prove that the Yang–Mills (YM) measure for the trivial principal bundle over the two‐dimensional torus, with any connected, compact structure group, is invariant for the associated renormalised Langevin dynamic. Our argument relies on a combination of regularity structures, lattice gauge‐fixing and Bourgain's method for invariant measures ...
Ilya Chevyrev, Hao Shen
wiley   +1 more source

Asymptotics of Symmetric Polynomials: A Dynamical Point of View. [PDF]

Commun Math Phys
Guionnet A, Huang J.
europepmc   +1 more source

Nonstationary Spatial Correlation of Earthquake Ground Motions in California

Earthquake Spectra, Volume 42, Issue 3, August 2026.
Assessing seismic risk to spatially distributed infrastructure systems requires realistic representations of spatially correlated ground motions. Existing models for the spatial correlations of ground motions rely on strong second‐order stationarity assumptions, under which the correlation structure is assumed to be invariant across space, potentially ...
Pengfei Wang, Busra Bocekli, Junhui Yang, Scott J. Brandenberg, Jonathan P. Stewart   +4 more
wiley   +1 more source

Relative Entropy Computations for Nonlinear Deformations of the Porous Steel Structures. [PDF]

Materials (Basel)
Strąkowski M, Kamiński M.
europepmc   +1 more source

Time Scale Decomposition of Stochastic Process Algebra Models

, 1997
Realistic models of computer and communication systems result in large, complex performance models. Compositionality, offered by stochastic process algebra constructs a model from submodels which are smaller and more tractable.
Vassilis Mertsiotakis
core  

AI in chemical engineering: From promise to practice

AIChE Journal, Volume 72, Issue 7, July 2026.
Abstract Artificial intelligence (AI) in chemical engineering has moved from promise to practice: physics‐aware (gray‐box) models are gaining traction, reinforcement learning complements model predictive control (MPC), and generative AI powers documentation, digitization, and safety workflows.
Jia Wei Chew, Ronnie Andersson, Thomas Bierweiler, Patrik Ryttestal, Torsten Wik   +4 more
wiley   +1 more source

Applying Quasi-Separability to Markovian Process Algebra

, 1998
Stochastic process algebras have become an accepted part of performance modelling over recent years. Because of the advantages of compositionality and flexibility they are increasingly being used to model larger and more complex systems.
Nigel Thomas, Stephen Gilmore
core  

A Multivariate Mixed‐Effects Regression Framework for Ground Motion Modeling: Integrating Parametric and Machine Learning Approaches

Earthquake Engineering &Structural Dynamics, Volume 55, Issue 9, Page 1811-1827, 25 July 2026.
ABSTRACT Multivariate ground motion models (GMMs) that capture the correlation between different intensity measures (IMs) are essential for seismic risk assessment. Conventional GMMs are often developed using a two‐stage approach, where separate univariate models with predefined functional forms are fitted first, and correlation is addressed in a ...
Sayed Mohammad Sajad Hussaini, Shaghayegh Karimzadeh, Paulo B. Lourenço   +2 more
wiley   +1 more source

Genetic prediction with ARG-powered linear algebra. [PDF]

Genetics
Lee H, Pope NS, Kelleher J, Gorjanc G, Ralph PL.   +4 more
europepmc   +1 more source

Coherent Forecasting of Realized Volatility

Journal of Forecasting, Volume 45, Issue 4, Page 1714-1729, July 2026.
ABSTRACT The QLIKE loss function is the stylized favorite of the literature on volatility forecasting when it comes to out‐of‐sample evaluation and the state of the art model for realized volatility (RV) forecasting is the HAR model, which minimizes the squared error loss for in‐sample estimation of the parameters.
Marius Puke, Karsten Schweikert
wiley   +1 more source