178 research outputs found
An Empirical Study Of Hospitality Management Student Attitudes Toward Group Projects: Instructional Factors And Team Problems
The development of positive attitudes in team-based work is important in management education. This study investigates hospitality students’ attitudes toward group projects by examining instructional factors and team problems. Specifically, we examine how the students’ perceptions of project appropriateness, instructors’ support, and evaluation fairness influence their attitudes toward group projects. Also the effect of students’ team problems on their attitudes toward group projects is examined. This study has highlighted the criticality of the instructor’s role in group project management for achieving a high level of positive attitudes toward group projects among the hospitality management students
Reduced-order modeling for parameterized PDEs via implicit neural representations
We present a new data-driven reduced-order modeling approach to efficiently
solve parametrized partial differential equations (PDEs) for many-query
problems. This work is inspired by the concept of implicit neural
representation (INR), which models physics signals in a continuous manner and
independent of spatial/temporal discretization. The proposed framework encodes
PDE and utilizes a parametrized neural ODE (PNODE) to learn latent dynamics
characterized by multiple PDE parameters. PNODE can be inferred by a
hypernetwork to reduce the potential difficulties in learning PNODE due to a
complex multilayer perceptron (MLP). The framework uses an INR to decode the
latent dynamics and reconstruct accurate PDE solutions. Further, a
physics-informed loss is also introduced to correct the prediction of unseen
parameter instances. Incorporating the physics-informed loss also enables the
model to be fine-tuned in an unsupervised manner on unseen PDE parameters. A
numerical experiment is performed on a two-dimensional Burgers equation with a
large variation of PDE parameters. We evaluate the proposed method at a large
Reynolds number and obtain up to speedup of O(10^3) and ~1% relative error to
the ground truth values.Comment: 9 pages, 5 figures, Machine Learning and the Physical Sciences
Workshop, NeurIPS 202
A fast and accurate domain-decomposition nonlinear manifold reduced order model
This paper integrates nonlinear-manifold reduced order models (NM-ROMs) with
domain decomposition (DD). NM-ROMs approximate the FOM state in a
nonlinear-manifold by training a shallow, sparse autoencoder using FOM snapshot
data. These NM-ROMs can be advantageous over linear-subspace ROMs (LS-ROMs) for
problems with slowly decaying Kolmogorov -width. However, the number of
NM-ROM parameters that need to trained scales with the size of the FOM.
Moreover, for "extreme-scale" problems, the storage of high-dimensional FOM
snapshots alone can make ROM training expensive. To alleviate the training
cost, this paper applies DD to the FOM, computes NM-ROMs on each subdomain, and
couples them to obtain a global NM-ROM. This approach has several advantages:
Subdomain NM-ROMs can be trained in parallel, each involve fewer parameters to
be trained than global NM-ROMs, require smaller subdomain FOM dimensional
training data, and training of subdomain NM-ROMs can tailor them to
subdomain-specific features of the FOM. The shallow, sparse architecture of the
autoencoder used in each subdomain NM-ROM allows application of hyper-reduction
(HR), reducing the complexity caused by nonlinearity and yielding computational
speedup of the NM-ROM. This paper provides the first application of NM-ROM
(with HR) to a DD problem. In particular, it details an algebraic DD
formulation of the FOM, trains a NM-ROM with HR for each subdomain, and
develops a sequential quadratic programming (SQP) solver to evaluate the
coupled global NM-ROM. Theoretical convergence results for the SQP method and a
priori and a posteriori error estimates for the DD NM-ROM with HR are provided.
The proposed DD NM-ROM with HR approach is numerically compared to a DD LS-ROM
with HR on 2D steady-state Burgers' equation, showing an order of magnitude
improvement in accuracy of the proposed DD NM-ROM over the DD LS-ROM
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