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For predictive modeling relying on Bayesian inversion, fully independent, or ``mean-field'', Gaussian distributions are often used as approximate probability density functions in variational inference since the number of variational…

Methodology · Statistics 2023-07-14 Wyatt Bridgman , Reese Jones , Mohammad Khalil

Many physical systems are well described on domains which are relatively large in some directions but relatively thin in other directions. In this scenario we typically expect the system to have emergent structures that vary slowly over the…

Dynamical Systems · Mathematics 2016-12-15 A. J. Roberts , J. E. Bunder

We review opportunities for stochastic geometric mechanics to incorporate observed data into variational principles, in order to derive data-driven nonlinear dynamical models of effects on the variability of computationally resolvable…

Chaotic Dynamics · Physics 2018-06-28 François Gay-Balmaz , Darryl D. Holm

The ability of widely used sampling methods, such as molecular dynamics or Monte Carlo, to explore complex free energy landscapes is severely hampered by the presence of kinetic bottlenecks. A large number of solutions have been proposed to…

Statistical Mechanics · Physics 2014-08-29 Omar Valsson , Michele Parrinello

The geometrical structure is among the most fundamental ingredients in understanding complex systems. Is there any systematic approach in defining structures quantitatively, rather than illustratively? If yes, what are the basic principles…

Fluid Dynamics · Physics 2020-05-27 Lipo Wang , Guiwen Tan , Hui Cao

A variational model to simultaneously treat Stress-Driven Rearrangement Instabilities, such as boundary discontinuities, internal cracks, external filaments, edge delamination, wetting, and brittle fractures, is introduced. The model is…

Analysis of PDEs · Mathematics 2020-06-24 Shokhrukh Yu. Kholmatov , Paolo Piovano

Extreme events are ubiquitous in a wide range of dynamical systems, including turbulent fluid flows, nonlinear waves, large scale networks and biological systems. Here, we propose a variational framework for probing conditions that trigger…

Fluid Dynamics · Physics 2018-07-10 Mohammad Farazmand , Themistoklis P. Sapsis

This paper introduces a model for the mechanical response of anisotropic soft materials undergoing large inelastic deformations. The composite is constituted by a soft isotropic matrix reinforced with stiff fibres, that can evolve…

Soft Condensed Matter · Physics 2022-07-25 Jacopo Ciambella , Paola Nardinocchi

We propose a multiscale method for elliptic problems on complex domains, e.g. domains with cracks or complicated boundary. For local singularities this paper also offers a discrete alternative to enrichment techniques such as XFEM. We…

Numerical Analysis · Mathematics 2016-11-01 Daniel Elfverson , Mats G. Larson , Axel Målqvist

We propose a general multiscale approach for the mechanical behavior of three-dimensional networks of macromolecules undergoing strain-induced unfolding. Starting from a (statistically based) energetic analysis of the macromolecule…

Soft Condensed Matter · Physics 2015-06-22 Domenico De Tommasi , Giuseppe Puglisi , Giuseppe Saccomandi

A variational model of pressure-dependent plasticity employing a time-incremental setting is introduced. A novel formulation of the dissipation potential allows one to construct the condensed energy in a variationally consistent manner. For…

Analysis of PDEs · Mathematics 2023-05-31 Florian Behr , Georg Dolzmann , Klaus Hackl , Ghina Jezdan

Coarse-grained models are a core computational tool in theoretical chemistry and biophysics. A judicious choice of a coarse-grained model can yield physical insight by isolating the essential degrees of freedom that dictate the…

Statistical Mechanics · Physics 2023-04-12 Shriram Chennakesavalu , David J. Toomer , Grant M. Rotskoff

Searching recurrent patterns in complex systems with high-dimensional phase spaces is an important task in diverse fields. In the current work, an improved scheme is proposed to accelerate the recently designed variational approach for…

Chaotic Dynamics · Physics 2022-01-11 Ding Wang , Yueheng Lan

In materials science, the challenge of rapid prototyping materials with desired properties often involves extensive experimentation to find suitable microstructures. Additionally, finding microstructures for given properties is typically an…

Machine Learning · Computer Science 2024-05-22 Sébastien Bompas , Stefan Sandfeld

Constructing numerical models of noisy partial differential equations is very delicate. Our long term aim is to use modern dynamical systems theory to derive discretisations of dissipative stochastic partial differential equations. As a…

Dynamical Systems · Mathematics 2007-05-23 A. J. Roberts

Flow-induced failure of granular materials is relevant to a broad range of geomechanical applications. Plasticity, which is the inherent failure mechanism of most granular materials, enables large deformations that can invalidate linearised…

Applied Physics · Physics 2019-08-26 Lucy C. Auton , Christopher W. MacMinn

This article presents a formulation that extends the variational multiscale modelling for compressible large-eddy simulation to a vast family of compact nodal numerical methods represented by the high-order flux reconstruction scheme. The…

Computational Physics · Physics 2020-02-19 Farshad Navah , Marta de la Llave Plata , Vincent Couaillier

Geophysical model domains typically contain irregular, complex fractal-like boundaries and physical processes that act over a wide range of scales. Constructing geographically constrained boundary-conforming spatial discretizations of these…

Geophysics · Physics 2017-03-27 Adam S. Candy

Granular systems confined in vertically vibrated shallow horizontal boxes (quasi two-dimensional geometry) present a liquid to solid phase transition when the frequency of the periodic forcing is increased. An effective model, where grains…

Statistical Mechanics · Physics 2018-08-08 Dino Risso , Rodrigo Soto , Marcelo Guzman

Coarse graining techniques play an essential role in accelerating molecular simulations of systems with large length and time scales. Theoretically grounded bottom-up models are appealing due to their thermodynamic consistency with the…

Computational Physics · Physics 2022-11-01 Blake R. Duschatko , Jonathan Vandermause , Nicola Molinari , Boris Kozinsky
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