8 citations · 10 across the 8 of their papers we have counts for
8 papers
A Generalisable Generative Model for Multi-Detector Calorimeter Simulation
Piyush Raikwar, Anna Zaborowska, Peter McKeown +3
Collider experiments, such as those at the Large Hadron Collider, use the Geant4 toolkit to simulate particle-detector interactions with high accuracy. However, these experiments i…
Sparse Sensor Allocation for Inverse Problems of Detecting Sparse Leaking Emission Sources
Xinchao Liu, Youngdeok Hwang, Dzung Phan +3
This paper investigates the sparse optimal allocation of sensors for detecting sparse leaking emission sources. Because of the non-negativity of emission rates, uncertainty associa…
Reducing data resolution for better super-resolution: Reconstructing turbulent flows from noisy observation
Kyongmin Yeo, Małgorzata J. Zimoń, Mykhaylo Zayats +1
A super-resolution (SR) method for the reconstruction of Navier-Stokes (NS) flows from noisy observations is presented. In the SR method, first the observation data is averaged ove…
Cascading Failure Prediction via Causal Inference
Shiuli Subhra Ghosh, Anmol Dwivedi, Ali Tajer +2
Causal inference provides an analytical framework to identify and quantify cause-and-effect relationships among a network of interacting agents. This paper offers a novel framework…
CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation
Claudius Krause, Michele Faucci Giannelli, Gregor Kasieczka +66
We present the results of the "Fast Calorimeter Simulation Challenge 2022" - the CaloChallenge. We study state-of-the-art generative models on four calorimeter shower datasets of i…
Optimal Sensor Allocation with Multiple Linear Dispersion Processes
Xinchao Liu, Dzung Phan, Youngdeok Hwang +3
This paper considers the optimal sensor allocation for estimating the emission rates of multiple sources in a two-dimensional spatial domain. Locations of potential emission source…
A 3D super-resolution of wind fields via physics-informed pixel-wise self-attention generative adversarial network
Takuya Kurihana, Kyongmin Yeo, Daniela Szwarcman +4
To mitigate global warming, greenhouse gas sources need to be resolved at a high spatial resolution and monitored in time to ensure the reduction and ultimately elimination of the…
A Supervised Contrastive Learning Pretrain-Finetune Approach for Time Series
Trang H. Tran, Lam M. Nguyen, Kyongmin Yeo +2
Foundation models have recently gained attention within the field of machine learning thanks to its efficiency in broad data processing. While researchers had attempted to extend t…