343 research outputs found
A Regularized Newton Method for Computing Ground States of Bose-Einstein condensates
In this paper, we propose a regularized Newton method for computing ground
states of Bose-Einstein condensates (BECs), which can be formulated as an
energy minimization problem with a spherical constraint. The energy functional
and constraint are discretized by either the finite difference, or sine or
Fourier pseudospectral discretization schemes and thus the original infinite
dimensional nonconvex minimization problem is approximated by a finite
dimensional constrained nonconvex minimization problem. Then an initial
solution is first constructed by using a feasible gradient type method, which
is an explicit scheme and maintains the spherical constraint automatically. To
accelerate the convergence of the gradient type method, we approximate the
energy functional by its second-order Taylor expansion with a regularized term
at each Newton iteration and adopt a cascadic multigrid technique for selecting
initial data. It leads to a standard trust-region subproblem and we solve it
again by the feasible gradient type method. The convergence of the regularized
Newton method is established by adjusting the regularization parameter as the
standard trust-region strategy. Extensive numerical experiments on challenging
examples, including a BEC in three dimensions with an optical lattice potential
and rotating BECs in two dimensions with rapid rotation and strongly repulsive
interaction, show that our method is efficient, accurate and robust.Comment: 25 pages, 6 figure
C2B Orders Decision-making in Multiple Supply Chains Under Cloud Manufacturing
Considering the background of cloud manufacturing and cluster supply chain, we build the basic model to assign the orders priority within each capacity. Then, considering the inter-chain horizontal cooperation, the extended model is proposed to parallel allocation of cross-chain orders as the orders exceeding one single- chain’s capacity. Lagrange algorithm is implemented, and the simulation analysis shown that the opportunity cost of rejected orders factor and cross-chain orders manufacturing cost factor have significant impacts on orders’ allocation decision, and there is a critical point in the combinations of those two factors. Through combinations, the cluster supply chain can make the acceptance decisions policy and production schedules of priority orders and cross- chain orders, so that customers’ satisfaction and the cluster supply chain’s total profits achieve the best situations
SeisCLIP: A seismology foundation model pre-trained by multi-modal data for multi-purpose seismic feature extraction
Training specific deep learning models for particular tasks is common across
various domains within seismology. However, this approach encounters two
limitations: inadequate labeled data for certain tasks and limited
generalization across regions. To address these challenges, we develop
SeisCLIP, a seismology foundation model trained through contrastive learning
from multi-modal data. It consists of a transformer encoder for extracting
crucial features from time-frequency seismic spectrum and an MLP encoder for
integrating the phase and source information of the same event. These encoders
are jointly pre-trained on a vast dataset and the spectrum encoder is
subsequently fine-tuned on smaller datasets for various downstream tasks.
Notably, SeisCLIP's performance surpasses that of baseline methods in event
classification, localization, and focal mechanism analysis tasks, employing
distinct datasets from different regions. In conclusion, SeisCLIP holds
significant potential as a foundational model in the field of seismology,
paving the way for innovative directions in foundation-model-based seismology
research.Comment: 27 pages, 9 figures, 4 table
mg2vec: Learning relationship-preserving heterogeneous graph representations via metagraph embedding
Seismic Foundation Model (SFM): a new generation deep learning model in geophysics
While computer science has seen remarkable advancements in foundation models,
which remain underexplored in geoscience. Addressing this gap, we introduce a
workflow to develop geophysical foundation models, including data preparation,
model pre-training, and adaption to downstream tasks. From 192 globally
collected 3-D seismic volumes, we create a carefully curated dataset of
2,286,422 2-D seismic images. Fully using these unlabeled images, we employ the
self-supervised learning to pre-train a Transformer-based Seismic Foundation
Model (SFM) for producing all-purpose seismic features that work across various
tasks and surveys. Through experiments on seismic facies classification,
geobody identification, interpolation, denoising, and inversion, our
pre-trained model demonstrates versatility, generalization, scalability, and
superior performance over baseline models. Conclusively, we provide a
foundation model and vast dataset to advance AI in geophysics, addressing
challenges (poor generalization, lacking labels, and repetitive training for
task-specified models) of applying AI in geophysics and paving the way for
future innovations in geoscience.Comment: 27 pages, 9 figures, and 4 table
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