49 research outputs found
Deep Random Vortex Method for Simulation and Inference of Navier-Stokes Equations
Navier-Stokes equations are significant partial differential equations that
describe the motion of fluids such as liquids and air. Due to the importance of
Navier-Stokes equations, the development on efficient numerical schemes is
important for both science and engineer. Recently, with the development of AI
techniques, several approaches have been designed to integrate deep neural
networks in simulating and inferring the fluid dynamics governed by
incompressible Navier-Stokes equations, which can accelerate the simulation or
inferring process in a mesh-free and differentiable way. In this paper, we
point out that the capability of existing deep Navier-Stokes informed methods
is limited to handle non-smooth or fractional equations, which are two critical
situations in reality. To this end, we propose the \emph{Deep Random Vortex
Method} (DRVM), which combines the neural network with a random vortex dynamics
system equivalent to the Navier-Stokes equation. Specifically, the random
vortex dynamics motivates a Monte Carlo based loss function for training the
neural network, which avoids the calculation of derivatives through
auto-differentiation. Therefore, DRVM not only can efficiently solve
Navier-Stokes equations involving rough path, non-differentiable initial
conditions and fractional operators, but also inherits the mesh-free and
differentiable benefits of the deep-learning-based solver. We conduct
experiments on the Cauchy problem, parametric solver learning, and the inverse
problem of both 2-d and 3-d incompressible Navier-Stokes equations. The
proposed method achieves accurate results for simulation and inference of
Navier-Stokes equations. Especially for the cases that include singular initial
conditions, DRVM significantly outperforms existing PINN method
The Lottery Ticket Hypothesis for Vision Transformers
The conventional lottery ticket hypothesis (LTH) claims that there exists a
sparse subnetwork within a dense neural network and a proper random
initialization method, called the winning ticket, such that it can be trained
from scratch to almost as good as the dense counterpart. Meanwhile, the
research of LTH in vision transformers (ViTs) is scarcely evaluated. In this
paper, we first show that the conventional winning ticket is hard to find at
weight level of ViTs by existing methods. Then, we generalize the LTH for ViTs
to input images consisting of image patches inspired by the input dependence of
ViTs. That is, there exists a subset of input image patches such that a ViT can
be trained from scratch by using only this subset of patches and achieve
similar accuracy to the ViTs trained by using all image patches. We call this
subset of input patches the winning tickets, which represent a significant
amount of information in the input. Furthermore, we present a simple yet
effective method to find the winning tickets in input patches for various types
of ViT, including DeiT, LV-ViT, and Swin Transformers. More specifically, we
use a ticket selector to generate the winning tickets based on the
informativeness of patches. Meanwhile, we build another randomly selected
subset of patches for comparison, and the experiments show that there is clear
difference between the performance of models trained with winning tickets and
randomly selected subsets
Deep Latent Regularity Network for Modeling Stochastic Partial Differential Equations
Stochastic partial differential equations (SPDEs) are crucial
for modelling dynamics with randomness in many areas including economics, physics, and atmospheric sciences. Recently, using deep learning approaches to learn the PDE solution for accelerating PDE simulation becomes increasingly
popular. However, SPDEs have two unique properties that
require new design on the models. First, the model to approximate the solution of SPDE should be generalizable over
both initial conditions and the random sampled forcing term.
Second, the random forcing terms usually have poor regularity whose statistics may diverge (e.g., the space-time white
noise). To deal with the problems, in this work, we design
a deep neural network called Deep Latent Regularity Net
(DLR-Net). DLR-Net includes a regularity feature block as
the main component, which maps the initial condition and the
random forcing term to a set of regularity features. The processing of regularity features is inspired by regularity structure theory and the features provably compose a set of basis to
represent the SPDE solution. The regularity features are then
fed into a small backbone neural operator to get the output.
We conduct experiments on various SPDEs including the dynamic Ξ¦^{4}_{1} model and the stochastic 2D Navier-Stokes equation to predict their solutions, and the results demonstrate that
the proposed DLR-Net can achieve SOTA accuracy compared
with the baselines. Moreover, the inference time is over 20
times faster than the traditional numerical solver and is comparable with the baseline deep learning models
Peeling the Onion: Hierarchical Reduction of Data Redundancy for Efficient Vision Transformer Training
Vision transformers (ViTs) have recently obtained success in many
applications, but their intensive computation and heavy memory usage at both
training and inference time limit their generalization. Previous compression
algorithms usually start from the pre-trained dense models and only focus on
efficient inference, while time-consuming training is still unavoidable. In
contrast, this paper points out that the million-scale training data is
redundant, which is the fundamental reason for the tedious training. To address
the issue, this paper aims to introduce sparsity into data and proposes an
end-to-end efficient training framework from three sparse perspectives, dubbed
Tri-Level E-ViT. Specifically, we leverage a hierarchical data redundancy
reduction scheme, by exploring the sparsity under three levels: number of
training examples in the dataset, number of patches (tokens) in each example,
and number of connections between tokens that lie in attention weights. With
extensive experiments, we demonstrate that our proposed technique can
noticeably accelerate training for various ViT architectures while maintaining
accuracy. Remarkably, under certain ratios, we are able to improve the ViT
accuracy rather than compromising it. For example, we can achieve 15.2% speedup
with 72.6% (+0.4) Top-1 accuracy on Deit-T, and 15.7% speedup with 79.9% (+0.1)
Top-1 accuracy on Deit-S. This proves the existence of data redundancy in ViT.Comment: AAAI 202
Path and Ridge Regression Analysis of Seed Yield and Seed Yield Components of Russian Wildrye (Psathyrostachys juncea Nevski) under Field Conditions
The correlations among seed yield components, and their direct and indirect
effects on the seed yield (Z) of Russina wildrye (Psathyrostachys
juncea Nevski) were investigated. The seed yield components:
fertile tillers m-2 (Y1), spikelets per fertile tillers
(Y2), florets per spikelet- (Y3), seed
numbers per spikelet (Y4) and seed weight (Y5) were
counted and the Z were determined in field experiments from 2003 to 2006 via big
sample size. Y1 was the most important seed yield component
describing the Z and Y2 was the least. The total direct effects of
the Y1, Y3 and Y5 to the Z were positive while
Y4 and Y2 were weakly negative. The total effects
(directs plus indirects) of the components were positively contributed to the Z
by path analyses. The seed yield components Y1, Y2,
Y4 and Y5 were significantly (P<0.001) correlated
with the Z for 4 years totally, while in the individual years, Y2
were not significant correlated with Y3, Y4 and
Y5 by Peason correlation analyses in the five components in the
plant seed production. Therefore, selection for high seed yield through direct
selection for large Y1, Y2 and Y3 would be
effective for breeding programs in grasses. Furthermore, it is the most
important that, via ridge regression, a steady algorithm model between Z and the
five yield components was founded, which can be closely estimated the seed yield
via the components
The Efficient and Convenient Extracting Uranium from Water by a Uranyl-Ion Affine Microgel Container
Uranium is an indispensable part of the nuclear industry that benefits us, but its consequent pollution of water bodies also makes a far-reaching impact on human society. The rapid, efficient and convenient extraction of uranium from water is to be a top priority. Thanks to the super hydrophilic and fast adsorption rate of microgel, it has been the ideal adsorbent in water; however, it was too difficult to recover the microgel after adsorption, which limited its practical applications. Here, we developed a uranyl-ion affine and recyclable microgel container that has not only the rapid swelling rate of microgel particles but also allows the detection of the adsorption saturation process by the naked eye