181 research outputs found
Microseismic source imaging using physics-informed neural networks with hard constraints
Microseismic source imaging plays a significant role in passive seismic
monitoring. However, such a process is prone to failure due to the aliasing
problem when dealing with sparse measured data. Thus, we propose a direct
microseismic imaging framework based on physics-informed neural networks
(PINNs), which can generate focused source images, even with very sparse
recordings. We use the PINNs to represent a multi-frequency wavefield and then
apply the inverse Fourier transform to extract the source image. Specially, we
modify the representation of the frequency-domain wavefield to inherently
satisfy the boundary conditions (the measured data on the surface) by means of
the hard constraint, which helps to avoid the difficulty in balancing the data
and PDE losses in PINNs. Furthermore, we propose the causality loss
implementation with respect to depth to enhance the convergence of PINNs. The
numerical experiments on the Overthrust model show that the method can admit
reliable and accurate source imaging for single- or multiple- sources and even
in passive monitoring settings. Then, we further apply our method on the
hydraulic fracturing field data, and demonstrate that our method can correctly
image the source
GaborPINN: Efficient physics informed neural networks using multiplicative filtered networks
The computation of the seismic wavefield by solving the Helmholtz equation is
crucial to many practical applications, e.g., full waveform inversion.
Physics-informed neural networks (PINNs) provide functional wavefield solutions
represented by neural networks (NNs), but their convergence is slow. To address
this problem, we propose a modified PINN using multiplicative filtered
networks, which embeds some of the known characteristics of the wavefield in
training, e.g., frequency, to achieve much faster convergence. Specifically, we
use the Gabor basis function due to its proven ability to represent wavefields
accurately and refer to the implementation as GaborPINN. Meanwhile, we
incorporate prior information on the frequency of the wavefield into the design
of the method to mitigate the influence of the discontinuity of the represented
wavefield by GaborPINN. The proposed method achieves up to a two-magnitude
increase in the speed of convergence as compared with conventional PINNs
Efficient physics-informed neural networks using hash encoding
Physics-informed neural networks (PINNs) have attracted a lot of attention in
scientific computing as their functional representation of partial differential
equation (PDE) solutions offers flexibility and accuracy features. However,
their training cost has limited their practical use as a real alternative to
classic numerical methods. Thus, we propose to incorporate multi-resolution
hash encoding into PINNs to improve the training efficiency, as such encoding
offers a locally-aware (at multi resolution) coordinate inputs to the neural
network. Borrowed from the neural representation field community (NeRF), we
investigate the robustness of calculating the derivatives of such hash encoded
neural networks with respect to the input coordinates, which is often needed by
the PINN loss terms. We propose to replace the automatic differentiation with
finite-difference calculations of the derivatives to address the discontinuous
nature of such derivatives. We also share the appropriate ranges for the hash
encoding hyperparameters to obtain robust derivatives. We test the proposed
method on three problems, including Burgers equation, Helmholtz equation, and
Navier-Stokes equation. The proposed method admits about a 10-fold improvement
in efficiency over the vanilla PINN implementation
A prior regularized full waveform inversion using generative diffusion models
Full waveform inversion (FWI) has the potential to provide high-resolution
subsurface model estimations. However, due to limitations in observation, e.g.,
regional noise, limited shots or receivers, and band-limited data, it is hard
to obtain the desired high-resolution model with FWI. To address this
challenge, we propose a new paradigm for FWI regularized by generative
diffusion models. Specifically, we pre-train a diffusion model in a fully
unsupervised manner on a prior velocity model distribution that represents our
expectations of the subsurface and then adapt it to the seismic observations by
incorporating the FWI into the sampling process of the generative diffusion
models. What makes diffusion models uniquely appropriate for such an
implementation is that the generative process retains the form and dimensions
of the velocity model. Numerical examples demonstrate that our method can
outperform the conventional FWI with only negligible additional computational
cost. Even in cases of very sparse observations or observations with strong
noise, the proposed method could still reconstruct a high-quality subsurface
model. Thus, we can incorporate our prior expectations of the solutions in an
efficient manner. We further test this approach on field data, which
demonstrates the effectiveness of the proposed method
Genetic Maps of Diploid Orchardgrass (\u3cem\u3eDactylis glomerata\u3c/em\u3e L.)
Orchardgrass (Dactylis glomerata L.) is indigenous to Eurasia and northern Africa. It has been naturalized on nearly every continent and is one of the top four economically important perennial forage grasses grown worldwide (Stewart and Ellison 2010). It has been used widely as forage due to its quality, biomass production and good shade tolerance. Despite its various agricultural uses, little information is available for functional and comparative genetic analysis and concomitant genetic improvement of this species. To date, a number of linkage maps have been constructed for forage grasses such as perennial ryegrass (Lolium perenne L.) (Jones et al. 2002), tall fescue (Lolium arundinaceum (Schreb.) Darbysh.) (Saha et al. 2005), and Zoysiagrass (Zoysia japonica) (Cai et al. 2005). Until recently, there have been no reports of genetic linkage studies in orchardgrass. In the present study, a genetic linkage map of diploid orchardgrass based on two-way pseudo-testcross mapping strategy was constructed using SRAP and SSR markers. This is the first step towards genomic mapping for this species
Nasopharyngeal carcinoma with non-squamous phenotype may be a variant of nasopharyngeal squamous cell carcinoma after inhibition of EGFR/PI3K/AKT/mTOR pathway
Nasopharyngeal carcinoma (NPC) is a cancerous tumor that develops in the nasopharynx epithelium and typically has squamous differentiation. The squamous phenotype is evident in immunohisto-chemistry, with diffuse nuclear positivity for p63 and p40. Nonetheless, a few NPCs have been identified by clinicopathological diagnosis that do not exhibit the squamous phenotype; these NPCs are currently referred to as non-squamous immuno-phenotype nasopharyngeal carcinomas (NSNPCs). In a previous work, we have revealed similarities between the histological appearance, etiology, and gene alterations of NSNPC and conventional NPC. According to ultrastructural findings, NSNPC still falls under the category of non-keratinized squamous cell carcinoma that is undifferentiated. NSNPC has an excellent prognosis and a low level of malignancy, according to a retrospective investigation. Based on prior research, we investigated the molecular mechanism of NSNPC not expressing the squamous phenotype and its biological behavior. IHC was used to determine the expression of EGFR, PI3K, AKT, p-AKT, mTOR, p-mTOR, Notch, STAT3 and p-STAT3 in a total of 20 NSNPC tissue samples and 20 classic NPC tissue samples. We obtained human NPC cell lines (CNE-2,5-8F) and used EGFR overexpression plasmid and shRNAs to transfect them. To find out whether mRNA and proteins were expressed in the cells, we used Western blotting and qRT-PCR. Cell biological behavior was discovered using the CCK-8 assay, cell migration assay, and cell invasion assay. EGFR, PI3K, p-AKT and p-mTOR proteins were lowly expressed in NSNPC tissues by immunohistochemistry, compared with classical NPC. In the classical NPC cell lines CNE-2 and 5-8F, overexpression EGFR can up-regulate the expression of p63 through the PI3K/AKT/mTOR pathway, and promote the proliferation, migration, and invasion of nasopharyngeal carcinoma cells. At the same time, knockout of EGFR can down-regulate p63 expression through the PI3K/AKT/mTOR pathway, and inhibit the proliferation, migration, and invasion of nasopharyngeal carcinoma cells. The lack of p63 expression in NSNPC was linked with the inhibition of the EGFR/PI3K/AKT/mTOR pathway, and NSNPC may be a variant of classical NPC
NeuralStagger: Accelerating Physics-constrained Neural PDE Solver with Spatial-temporal Decomposition
Neural networks have shown great potential in accelerating the solution of
partial differential equations (PDEs). Recently, there has been a growing
interest in introducing physics constraints into training neural PDE solvers to
reduce the use of costly data and improve the generalization ability. However,
these physics constraints, based on certain finite dimensional approximations
over the function space, must resolve the smallest scaled physics to ensure the
accuracy and stability of the simulation, resulting in high computational costs
from large input, output, and neural networks. This paper proposes a general
acceleration methodology called NeuralStagger by spatially and temporally
decomposing the original learning tasks into several coarser-resolution
subtasks. We define a coarse-resolution neural solver for each subtask, which
requires fewer computational resources, and jointly train them with the vanilla
physics-constrained loss by simply arranging their outputs to reconstruct the
original solution. Due to the perfect parallelism between them, the solution is
achieved as fast as a coarse-resolution neural solver. In addition, the trained
solvers bring the flexibility of simulating with multiple levels of resolution.
We demonstrate the successful application of NeuralStagger on 2D and 3D fluid
dynamics simulations, which leads to an additional speed-up.
Moreover, the experiment also shows that the learned model could be well used
for optimal control.Comment: ICML 2023 accepte
Phylogenetic Relationships in the Festuca-Lolium Complex (Loliinae; Poaceae): New Insights from Chloroplast Sequences
The species within the Lolium/Festuca grass complex have dispersed and colonized large areas of temperate global grasslands both naturally and by human intervention. The species within this grass complex represent some of the most important grass species both for amenity and agricultural use worldwide. There has been renewed interest by grass breeders in producing hybrid combinations between these species and several countries now market Festulolium varieties as a combination of genes from both genera. The two genera have been differentiated by their inflorescence structure, but controversy has surrounded the taxonomic classification of the Lolium-Festuca complex species for several decades. In order to better understand the complexities within the Lolium/Festuca complex and their genetic background, the phylogeny of important examplers from the Lolium-Festuca complex were reconstructed. In total 40 taxa representing the Festuca and Lolium species with Vulpia myuros and Brachypodium distachyon as outgroups were sampled, using two noncoding intergenic spacers (trnQ-rps16, trnH-psbA) and one coding gene (rbcL). Maximum parsimony (MP), Bayesian inference (BI) analyses based on each partition and combined plastid DNA dataset, and median-jointing network analysis were employed. The outcomes strongly suggested that the subgen. Schedonorus has a close relationship to Lolium, and it is also proposed to move the sect. Leucopoa from subgen. Leucopoa to Subgen. Schedonorus and to separate sect. Breviaristatae from the subgen. Leucopoa. We found that F. californica could be a lineage of hybrid origin because of its intermediate placement between the broad-leaved and fine-leaved clade
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