336 research outputs found
Micro-Macro Modeling of Polymeric Fluids and Shear-Induced Microscopic Behaviors
This article delves into the micro-macro modeling of polymeric fluids,
considering various microscopic potential energies, including the classical
Hookean potential, as well as newly proposed modified Morse and Elastic-plastic
potentials. These proposed potentials encompass microscopic-scale bond-breaking
processes. The development of a thermodynamically consistent micro-macro model
is revisited, employing the energy variational method. To validate the model's
predictions, we conduct numerical simulations utilizing a deterministic
particle-FEM method. Our numerical findings shed light on the distinct
behaviors exhibited by polymer chains at the micro-scale in comparison to the
macro-scale velocity and induced shear stresses of fluids under shear flow.
Notably, we observe that polymer elongation, rotation, and bond breaking
contribute to the zero polymer-induced stress in the micro-macro model when
employing Morse and Elastic-plastic potentials. Furthermore, at high shear
rates, polymer rotation is found to induce shear-thinning behavior in the model
employing the classical Hookean potential
A Bubble Model for the Gating of Kv Channels
Voltage-gated Kv channels play fundamental roles in many biological
processes, such as the generation of the action potential. The gating mechanism
of Kv channels is characterized experimentally by single-channel recordings and
ensemble properties of the channel currents. In this work, we propose a bubble
model coupled with a Poisson-Nernst-Planck (PNP) system to capture the key
characteristics, particularly the delay in the opening of channels. The coupled
PNP system is solved numerically by a finite-difference method and the solution
is compared with an analytical approximation. We hypothesize that the
stochastic behaviour of the gating phenomenon is due to randomness of the
bubble and channel sizes. The predicted ensemble average of the currents under
various applied voltages across the channels is consistent with experimental
observations, and the Cole-Moore delay is captured by varying the holding
potential
An energy stable C<sup>0</sup> finite element scheme for a quasi-incompressible phase-field model of moving contact line with variable density
In this paper, we focus on modeling and simulation of two-phase flow with
moving contact lines and variable density. A thermodynamically consistent
phase-field model with General Navier Boundary Condition is developed based on
the concept of quasi-incompressibility and the energy variational method. Then
a mass conserving and energy stable C0 finite element scheme is developed to
solve the PDE system. Various numerical simulation results show that the
proposed schemes are mass conservative, energy stable and the 2nd order for P1
element and 3rd order for P2 element convergence rate in the sense of L2 norm
High-Resolution Deep Image Matting
Image matting is a key technique for image and video editing and composition.
Conventionally, deep learning approaches take the whole input image and an
associated trimap to infer the alpha matte using convolutional neural networks.
Such approaches set state-of-the-arts in image matting; however, they may fail
in real-world matting applications due to hardware limitations, since
real-world input images for matting are mostly of very high resolution. In this
paper, we propose HDMatt, a first deep learning based image matting approach
for high-resolution inputs. More concretely, HDMatt runs matting in a
patch-based crop-and-stitch manner for high-resolution inputs with a novel
module design to address the contextual dependency and consistency issues
between different patches. Compared with vanilla patch-based inference which
computes each patch independently, we explicitly model the cross-patch
contextual dependency with a newly-proposed Cross-Patch Contextual module (CPC)
guided by the given trimap. Extensive experiments demonstrate the effectiveness
of the proposed method and its necessity for high-resolution inputs. Our HDMatt
approach also sets new state-of-the-art performance on Adobe Image Matting and
AlphaMatting benchmarks and produce impressive visual results on more
real-world high-resolution images.Comment: AAAI 202
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