11,119 research outputs found
MeshfreeFlowNet: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework
We propose MeshfreeFlowNet, a novel deep learning-based super-resolution
framework to generate continuous (grid-free) spatio-temporal solutions from the
low-resolution inputs. While being computationally efficient, MeshfreeFlowNet
accurately recovers the fine-scale quantities of interest. MeshfreeFlowNet
allows for: (i) the output to be sampled at all spatio-temporal resolutions,
(ii) a set of Partial Differential Equation (PDE) constraints to be imposed,
and (iii) training on fixed-size inputs on arbitrarily sized spatio-temporal
domains owing to its fully convolutional encoder. We empirically study the
performance of MeshfreeFlowNet on the task of super-resolution of turbulent
flows in the Rayleigh-Benard convection problem. Across a diverse set of
evaluation metrics, we show that MeshfreeFlowNet significantly outperforms
existing baselines. Furthermore, we provide a large scale implementation of
MeshfreeFlowNet and show that it efficiently scales across large clusters,
achieving 96.80% scaling efficiency on up to 128 GPUs and a training time of
less than 4 minutes.Comment: Supplementary Video: https://youtu.be/mjqwPch9gDo. Accepted to SC2
Identifying Real Estate Opportunities using Machine Learning
The real estate market is exposed to many fluctuations in prices because of
existing correlations with many variables, some of which cannot be controlled
or might even be unknown. Housing prices can increase rapidly (or in some
cases, also drop very fast), yet the numerous listings available online where
houses are sold or rented are not likely to be updated that often. In some
cases, individuals interested in selling a house (or apartment) might include
it in some online listing, and forget about updating the price. In other cases,
some individuals might be interested in deliberately setting a price below the
market price in order to sell the home faster, for various reasons. In this
paper, we aim at developing a machine learning application that identifies
opportunities in the real estate market in real time, i.e., houses that are
listed with a price substantially below the market price. This program can be
useful for investors interested in the housing market. We have focused in a use
case considering real estate assets located in the Salamanca district in Madrid
(Spain) and listed in the most relevant Spanish online site for home sales and
rentals. The application is formally implemented as a regression problem that
tries to estimate the market price of a house given features retrieved from
public online listings. For building this application, we have performed a
feature engineering stage in order to discover relevant features that allows
for attaining a high predictive performance. Several machine learning
algorithms have been tested, including regression trees, k-nearest neighbors,
support vector machines and neural networks, identifying advantages and
handicaps of each of them.Comment: 24 pages, 13 figures, 5 table
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