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The automated construction of coarse-grained models represents a pivotal component in computer simulation of physical systems and is a key enabler in various analysis and design tasks related to uncertainty quantification. Pertinent methods…

Machine Learning · Statistics 2019-09-11 Constantin Grigo , Phaedon-Stelios Koutsourelakis

This is our current research perspective on models providing insight into statistical mechanics. It is necessarily personal, emphasizing our own interest in simulation as it developed from the National Laboratories' work to the worldwide…

Statistical Mechanics · Physics 2020-12-30 William Graham Hoover , Carol Griswold Hoover

Efficient sampling of the Boltzmann distribution of molecular systems is a long-standing challenge. Recently, instead of generating long molecular dynamics simulations, generative machine learning methods such as normalizing flows have been…

Machine Learning · Computer Science 2024-08-06 Henrik Schopmans , Pascal Friederich

Soft robot are celebrated for their propensity to enable compliant and complex robot-environment interactions. Soft robotic manipulators, or slender continuum structure robots have the potential to exploit these interactions to enable new…

Robotics · Computer Science 2022-11-21 Francesco Stella , Qinghua Guan , Jinsong Leng , Cosimo Della Santina , Josie Hughes

We demonstrate how the dynamical coarse-graining approach can be systematically extended to higher orders in the coupling between system and reservoir. Up to second order in the coupling constant we explicitly show that dynamical…

Quantum Physics · Physics 2009-03-23 Gernot Schaller , Philipp Zedler , Tobias Brandes

Facilitated or kinetically constrained spin models (KCSM) are a class of interacting particle systems reversible w.r.t. to a simple product measure. Each dynamical variable (spin) is re-sampled from its equilibrium distribution only if the…

Probability · Mathematics 2012-10-04 Nicoletta Cancrini , Fabio Martinelli , Cyril Roberto , Cristina Toninelli

Multiscale systems are ubiquitous in science and technology, but are notoriously challenging to simulate as short spatiotemporal scales must be appropriately linked to emergent bulk physics. When expensive high-dimensional dynamical systems…

Machine Learning · Computer Science 2025-12-30 Quercus Hernandez , Max Win , Thomas C. O'Connor , Paulo E. Arratia , Nathaniel Trask

Filtering is concerned with the sequential estimation of the state, and uncertainties, of a Markovian system, given noisy observations. It is particularly difficult to achieve accurate filtering in complex dynamical systems, such as those…

Probability · Mathematics 2015-12-14 Wonjung Lee , Andrew Stuart

Quantum nanosystems such as graphene nanoribbons or superconducting nanoparticles are studied via a multiscale approach. Long space-time dynamics is derived using a perturbation expansion in the ratio of the nearest-neighbor distance to a…

Quantum Physics · Physics 2011-03-10 Zeina Shreif , Peter Ortoleva

The control of granular materials, showing up in many industrial applications, is a challenging open research problem. Granular material systems are complex-behavior (as they could have solid-, fluid-, and gas-like behaviors) and…

Optimization and Control · Mathematics 2023-02-08 Yuichiro Aoyama , Amin Haeri , Evangelos A. Theodorou

Due to computational constraints, running global climate models (GCMs) for many years requires a lower spatial grid resolution (${\gtrsim}50$ km) than is optimal for accurately resolving important physical processes. Such processes are…

Numerical simulations provide key insights into many physical, real-world problems. However, while these simulations are solved on a full 3D domain, most analysis only require a reduced set of metrics (e.g. plane-level concentrations). This…

Computational Physics · Physics 2025-11-27 Tingkai Xue , Chin Chun Ooi , Zhengwei Ge , Fong Yew Leong , Hongying Li , Chang Wei Kang

Although coarse-grained models have been widely used to explain exotic phenomena in complex fluids, such as droplet formation in living cells, these conventional approaches often fail to capture the intricate microscopic degrees of freedom…

Soft Condensed Matter · Physics 2025-06-13 Masanari Shimada , Tetsuya J. Kobayashi

Given that the physical properties of polymeric liquids extend on a wide range of lengthscales, it is computationally convenient to represent them by coarse-grained (CG) descriptions at various granularities to investigate local and global…

Soft Condensed Matter · Physics 2019-06-06 Mohammadhasan Dinpajooh , Marina G. Guenza

The discovery of new functional and stable materials is a big challenge due to its complexity. This work aims at the generation of new crystal structures with desired properties, such as chemical stability and specified chemical…

Computational Physics · Physics 2023-10-18 Arsen Sultanov , Jean-Claude Crivello , Tabea Rebafka , Nataliya Sokolovska

We introduce a general framework for constructing coarse-grained potential models without ad hoc approximations such as limiting the potential to two- and/or three-body contributions. The scheme, called Deep Coarse-Grained Potential…

Chemical Physics · Physics 2018-08-15 Linfeng Zhang , Jiequn Han , Han Wang , Roberto Car , Weinan E

One of the main elements of the self-consistent generalized Langevin equation (SCGLE) theory of colloid dynamics [Phys. Rev. E {\bf 62}, 3382 (2000); ibid {\bf 72}, 031107 (2005)] is the introduction of exact short-time moment conditions in…

We review a few representative examples of granular experiments or models where phase separation, accompanied by domain coarsening, is a relevant phenomenon. We first elucidate the intrinsic non-equilibrium, or athermal, nature of granular…

Statistical Mechanics · Physics 2015-04-29 Andrea Baldassarri , Andrea Puglisi , Alessandro Sarracino

We present a detailed derivation and testing of our approach to rescale the dynamics of mesoscale simulations of coarse-grained polymer melts (I. Y. Lyubimov et al. J. Chem. Phys. \textbf{132}, 11876, 2010). Starting from the…

Soft Condensed Matter · Physics 2015-05-27 I. Y. Lyubimov , M. G. Guenza

In molecular dynamics and sampling of high dimensional Gibbs measures coarse-graining is an important technique to reduce the dimensionality of the problem. We will study and quantify the coarse-graining error between the coarse-grained…

Analysis of PDEs · Mathematics 2020-06-04 M. H. Duong , A. Lamacz , M. A. Peletier , A. Schlichting , U. Sharma
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