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research.html
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<!DOCTYPE html>
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<h3>Research</h3>
<!-- <div style="clear:both; float: left; width: 695px;">
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<div class="sec">Reinforcement for Combinatorial Optimization</div>
<img width=100% style="padding-left: 0%; padding-right: 0%;" src="img/rl4co-titlefig.png" alt=""/>
<h5>RL4CO</h5>
<p>
An extensive Reinforcement Learning (RL) for Combinatorial Optimization (CO) benchmark.
Our goal is to provide a unified framework for RL-based CO algorithms, and to facilitate
reproducible research in this field, decoupling the science from the engineering.
RL4CO is built upon:
TorchRL: official PyTorch framework for RL algorithms and vectorized environments on GPUs;
TensorDict: a library to easily handle heterogeneous data such as states, actions and rewards;
PyTorch Lightning: a lightweight PyTorch wrapper for high-performance AI research;
Hydra: a framework for elegantly configuring complex applications.
</p>
<a href="https://arxiv.org/abs/2306.17100">[Paper]</a>
<a href="https://github.com/kaist-silab/rl4co">[GitHub]</a>
<br/><br/>
<div class="sec">Scientific Computing</div>
<img width=100% style="padding-left: 0%; padding-right: 0%;" src="img/graph-spline-nets-titlefig.png" alt=""/>
<h5>GraphSplineNets</h5>
<p>
We present GraphSplineNets, a novel deep-learning method to speed up the simulation of physical
systems by reducing the grid size and number of iteration steps of deep surrogate models.
Our method uses two differentiable orthogonal spline collocation methods to efficiently generate
continuous solutions at any location in time and space. Additionally, we introduce an adaptive collocation
strategy in space to prioritize sampling from the most important regions. GraphSplineNets improve the
accuracy-speedup tradeoff in forecasting various dynamical systems with increasing complexity,
including the heat equation, damped wave propagation, incompressible Navier-Stokes equations,
and real-world ocean currents in both regular and irregular domains.
</p>
<a href="https://neurips.cc/virtual/2023/poster/71917">[Paper]</a>
<a href="https://github.com/kaist-silab/graphsplinenets">[GitHub]</a>
<br/><br/>
<div class="sec">Robot Navigation</div>
<img width=100% style="padding-left: 0%; padding-right: 0%;" src="img/robot-navigation-titlefig.png" alt=""/>
<h5>EvolveHyperGraph</h5>
<p>
In this paper, we propose a group-aware relational reasoning approach with explicit inference of the underlying
dynamically evolving relational structures, and we demonstrate its effectiveness for multi-agent trajectory prediction.
In addition to the edges between a pair of nodes (i.e., agents), we propose to infer hyperedges that adaptively
connect multiple nodes to enable group-aware relational reasoning in an unsupervised manner without fixing the number of hyperedges.
The proposed approach infers the dynamically evolving relation graphs and hypergraphs over time to capture the
evolution of relations, The proposed approach infers the dynamically evolving relation graphs and hypergraphs over
time to capture the evolution of relations, which the trajectory predictor uses to obtain future states.
</p>
<a href="https://arxiv.org/abs/2208.05470">[Paper]</a>
<a href="https://github.com/cbhua/model-evolvehypergraph">[GitHub]</a>
<br/><br/>
<div class="sec">Water Research</div>
<img width=100% style="padding-left: 0%; padding-right: 0%;" src="img/water-research-titlefig.png" alt=""/>
<h5>Water Treatment Data Analysis</h5>
<p>
This study investigated the use of machine learning models, including traditional and deep learning approaches,
for predicting both coagulant dosage and settled water turbidity in the water treatment process using six years
of operating data. The study found that deep learning models, which process temporal sequential data, significantly
improved prediction accuracies in response to changing dynamics of water treatment processes. The results emphasize
the importance of collecting large datasets for modeling water treatment processes to capture rapid changes in raw
water quality, thereby increasing prediction accuracies. The modeling results provide suggestions for model selection,
data collection, and monitoring implementation in water treatment plants, which can enhance the accuracy of predictions
and ensure high-quality treated water.
</p>
<a href="https://www.sciencedirect.com/science/article/pii/S0043135423001008">[Paper]</a>
<a href="https://github.com/cbhua/coagulant-forecast">[GitHub]</a>
<br/>
<h5>Time-series Data Prediction</h5>
<p>
A graph attention multivariate time series forecasting (GAMTF) model was developed to determine coagulant dosage
and was compared with conventional machine learning and deep learning models. The GAMTF model (R2 = 0.94, RMSE = 3.55)
outperformed the other models (R2 = 0.63 - 0.89, RMSE = 4.80 - 38.98), and successfully predicted both coagulant dosage
and settled water turbidity simultaneously. The GAMTF model improved the prediction accuracy by considering the hidden
interrelationships between features and the past states of features. The results demonstrate the first successful application
of multivariate time series deep learning model, especially, a state-of-the-art graph attention-based model, using long-term
data for decision-support systems in water treatment processes.
</p>
<a href="https://www.sciencedirect.com/science/article/pii/S2214714423004683">[Paper]</a>
<a href="https://github.com/cbhua/coagulant-forecast">[GitHub]</a>
<br/><br/>
<div class="sec">Anomaly Detection</div>
<h5>BSincNet</h5>
<p>
In this work, using the close relationship between time-series and its spectrum on frequency-domain, we propose a domain
generalization method for time-series classification. Specifically, we quantify latent frequency spaces that captures
the distinct characteristics of time-series data per each label and domains. Then, we use the filtered time-series on the
assigned latent frequency spaces as augmented training sets for the model to make the model invariant classification up to
each domain of the time-series. We demonstrate the proposed method improves the domain generalization performance on various
time-series classification tasks.
</p>
<a href="">[Paper]</a>
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2023 Chuanbo Hua | Industrial & Systems Engineering | Korea Advanced Institute of Science & Technology
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