Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 1507.06643

New parallelizable schemes for integrating the Dissipative Particle Dynamics with Energy Conservation

A. -A. Homman·J. -B. Maillet·J. Roussel·G. Stoltz
View original source ↗Read source document ↗Export metadata ↓

Abstract

This work presents new parallelizable numerical schemes for the integration of Dissipative Particle Dynamics with Energy conservation (DPDE). So far, no numerical scheme introduced in the literature is able to correctly preserve the energy over long times and give rise to small errors on average properties for moderately small timesteps, while being straightforwardly parallelizable. We present in this article two new methods, both straightforwardly parallelizable, allowing to correctly preserve the total energy of the system. We illustrate the accuracy and performance of these new schemes both on equilibrium and nonequilibrium parallel simulations.

Explore related subjects

physics.comp-phcond-mat.stat-mech

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

A. -A. Homman, J. -B. Maillet, J. Roussel, G. Stoltz. 2015-11-30. New parallelizable schemes for integrating the Dissipative Particle Dynamics with Energy Conservation. https://doi.org/10.1063/1.4937797

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Data-Driven Cohesive Zone Modeling within the Generalized Standard Materials Framework

Cohesive zone models are widely used to describe fracture and interfacial failure, yet most formulations prescribe problem-specific analytical traction-separation laws together with phenomenological rules for unloading and reloading, leading to specialized models for different cohesive behaviors. This work develops a unified learnable cohesive formulation within the generalized standard materials framework, in which the response is generated from learned constitutive functions while the underlying thermodynamic structure remains fixed. The surface free energy is decomposed into active and contact contributions, and irreversible damage evolution is governed by a learned mode-dependent damage resistance. The active energy is represented by an input-convex neural network, while the inverse damage resistance is represented by a monotone neural network. Convexity, monotonicity, normalization, and damage irreversibility are incorporated directly into the constitutive representation. Direct parameterization of the inverse resistance yields an explicit damage update and avoids local nonlinear inversion during constitutive evaluation. Material-point studies show that the formulation can represent qualitatively distinct cohesive responses, including plateaus, extended softening tails, irregular softening, nonlinear unloading, distinct Mode I and Mode II behaviors, and several classical mixed-mode cohesive laws. The framework therefore replaces law-specific model construction with a single thermodynamically structured representation capable of learning cohesive responses of broad functional complexity from data.

physics.comp-ph↗
arXiv2026

Physics Based Triple Debye Dielectric Modeling and Multi Layer ADE FDTD Simulation of Terahertz Pulse Reflection for Breast Cancer Detection

Terahertz (THz) imaging has emerged as a promising non-ionizing modality for breast cancer assessment owing to its intrinsic sensitivity to tissue hydration. However, existing dielectric descriptions of biological tissue, largely restricted to single- or double-Debye models, fail to capture the multiscale relaxation dynamics governing broadband THz dispersion and absorption, thereby limiting the quantitative interpretation of reflected pulse signatures.Here, a physics based triple Debye dielectric framework is developed to quantitatively predict broadband THz-tissue interactions. The proposed model explicitly incorporates three physically distinct relaxation processes associated with free-water rotational dynamics, bound-water relaxation (τ_2), and ultrafast interfacial/macromolecular polarization (τ_3). Model parameters are obtained by nonlinear least-squares fitting to experimentally measured refractive-index data digitized from published THz time-domain spectroscopy measurements of ex vivo human breast tissue. Compared with conventional Debye formulations, the proposed model substantially improves the fitting accuracy, reducing the root-mean-square error from 0.7773 (single Debye) and 0.2976 (double Debye) to only 0.0199 for the triple-Debye model. Fullwave FDTD ADE simulations of realistic multilayer breast structures further demonstrate that reflected THz pulses encode tissue hydration through reproducible temporal signatures, including increased reflection amplitude, delayed pulse arrival, and enhanced waveform broadening in malignant tissue. Polarization and angle resolved Fresnel analysis further indicates that hydration-dependent pseudo-Brewster minima provide an additional contrast mechanism by selectively suppressing reflections from low hydration normal and adipose tissues.

physics.comp-ph↗
arXiv2026

Physics-enriched neural solvers for transient ice-flow simulation

Transient glacier simulations with higher-order ice flow require the repeated solution of a nonlinear problem as the geometry evolves. In the online mode of the Instructed Glacier Model, the velocity field is represented by a neural network whose weights are warm-started from the previous time step and updated with a few optimizer iterations. We show that supplying the network with inexpensive input fields derived from low-order ice-flow balances improves this online solver. Unlike residual-based physics-informed neural networks, which incorporate physics through governing-equation penalties in the loss, our approach leaves the governing energy objective unchanged, adding physical structure through the network inputs. Across three real-world glacier configurations, the enriched solver is markedly more robust to solver settings. On the two alpine cases, it also improves the tuned accuracy--runtime trade-off, reducing surface-velocity errors by factors of two to four at fixed runtime and reaching few-percent relative errors with only $10^4$--$10^5$ trainable parameters, far fewer than comparable raw-input baselines. A 300-year Aletsch simulation then completes in under one minute, and the larger Valais domain in about two minutes, on a single GPU---a budget once reserved for much simpler shallow-ice models. Gains are smaller for the fast marine-terminating glacier, where nonlocal stress coupling favors larger or spectral networks. More broadly, the results suggest that enriching a neural solver's inputs with reduced-order physics can make repeated higher-order solves much cheaper, with no training data and no offline training.

physics.comp-ph↗