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268 results on '"Seong, Woojae"'

1. Identifying Physical Law of Hamiltonian Systems via Meta-Learning

Author

Lee, Seungjun, Yang, Haesang, and Seong, Woojae

Subjects
Computer Science - Machine Learning, Physics - Computational Physics
Abstract

Hamiltonian mechanics is an effective tool to represent many physical processes with concise yet well-generalized mathematical expressions. A well-modeled Hamiltonian makes it easy for researchers to analyze and forecast many related phenomena that are governed by the same physical law. However, in general, identifying a functional or shared expression of the Hamiltonian is very difficult. It requires carefully designed experiments and the researcher's insight that comes from years of experience. We propose that meta-learning algorithms can be potentially powerful data-driven tools for identifying the physical law governing Hamiltonian systems without any mathematical assumptions on the representation, but with observations from a set of systems governed by the same physical law. We show that a well meta-trained learner can identify the shared representation of the Hamiltonian by evaluating our method on several types of physical systems with various experimental settings., Comment: ICLR 2021

Published
2021

4. Predicting ocean pressure field with a physics-informed neural network.

Author

Yoon, Seunghyun, Park, Yongsung, Gerstoft, Peter, and Seong, Woojae

Abstract

Ocean sound pressure field prediction, based on partially measured pressure magnitudes at different range-depths, is presented. Our proposed machine learning strategy employs a trained neural network with range-depth as input and outputs complex acoustic pressure at the location. We utilize a physics-informed neural network (PINN), fitting sampled data while considering the additional information provided by the partial differential equation (PDE) governing the ocean sound pressure field. In vast ocean environments with kilometer-scale ranges, pressure fields exhibit rapidly fluctuating phases, even at frequencies below 100 Hz, posing a challenge for neural networks to converge to accurate solutions. To address this, we utilize the envelope function from the parabolic-equation technique, fundamental in ocean sound propagation modeling. The envelope function shows slower variations across ranges, enabling PINNs to predict sound pressure in an ocean waveguide more effectively. Additional PDE information allows PINNs to capture PDE solutions even with a limited amount of training data, distinguishing them from purely data-driven machine learning approaches that require extensive datasets. Our approach is validated through simulations and using data from the SWellEx-96 experiment. [ABSTRACT FROM AUTHOR]

Published
2024
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32. Three-dimensional acoustic parabolic equation model based on GPU processing

Author

Lee, Keunhwa, Seong, Woojae, and Na, Youngnam

Abstract

Proceedings of the ICA 2019 and EAA Euroregio : 23rd International Congress on Acoustics, integrating 4th EAA Euroregio 2019 : 9-13 September 2019, Aachen, Germany / proceedings editor: Martin Ochmann, Michael Vorländer, Janina Fels 23rd International Congress on Acoustics, integrating 4th EAA Euroregio 2019, ICA 2019, Aachen, Germany, 9 Sep 2019 - 13 Sep 2019; Aachen (2019)., Published by Aachen

Published
2019

46. Reverberation Level Modeling via Coupled Mode Approach in Shallow-Water Sound Channel with Internal Solitary Waves.

Author

Park, Jungyong, Yang, Haesang, Seong, Woojae, and Choo, Youngmin

Subjects
UNDERWATER acoustics, SOLITONS, MARINE ecology, INTERNAL waves, SPEED of sound
Abstract

Reverberation level (RL) is modeled in a shallow water environment with an underwater sound channel and internal solitary wave (ISW) using the coupled mode method. Numerical RL result based on one-way coupled mode shows an abnormal increase when a source is located near the channel axis and the ISW is located far from the source. The abnormal increase is analyzed by using a two-mode approach (assuming a trapped mode and a bottom interacting mode). The two-mode approach explains the relation between the RL increase and the ISW location explicitly: the ISW transfers trapped mode energy to bottom interacting mode energy, and its increasing rate is a function of its modal attenuation and ISW location from the source. The sensitivity test according to several ISW parameters is also performed. [ABSTRACT FROM AUTHOR]

Published
2019
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