136 citations · 143 across the 10 of their papers we have counts for
10 papers
Linear and Nonlinear Latent-Space Reduced-Order Models for the Rayleigh--Taylor Instability
Téo Granger, Balu Nadiga, Benoît-Joseph Gréa +2
We use a large database of direct numerical simulations to investigate the transition of the Rayleigh--Taylor instability to turbulence and its evolution toward a late-time self-si…
Revealing Low-Dimensional Structure in 2D Richtmyer-Meshkov Instabilities via Parametric Reduced-Order Modeling
Daniel Messenger, Daniel Serino, Balu Nadiga +1
Efficient modeling of the Richtmyer-Meshkov instability (RMI) is essential to many engineering tasks, including high-speed combustion and drive and capsule geometry optimization in…
Ensembles of Neural Surrogates for Parametric Sensitivity in Ocean Modeling
Yixuan Sun, Romain Egele, Sri Hari Krishna Narayanan +6
Accurate simulations of the oceans are crucial in understanding the Earth system. Despite their efficiency, simulations at lower resolutions must rely on various uncertain paramete…
Generalizable Implicit Neural Representations via Parameterized Latent Dynamics for Baroclinic Ocean Forecasting
Guang Zhao, Xihaier Luo, Seungjun Lee +7
Mesoscale ocean dynamics play a critical role in climate systems, governing heat transport, hurricane genesis, and drought patterns. However, simulating these processes at high res…
Studying the Impact of Latent Representations in Implicit Neural Networks for Scientific Continuous Field Reconstruction
Wei Xu, Derek Freeman DeSantis, Xihaier Luo +5
Learning a continuous and reliable representation of physical fields from sparse sampling is challenging and it affects diverse scientific disciplines. In a recent work, we present…
Continuous Field Reconstruction from Sparse Observations with Implicit Neural Networks
Xihaier Luo, Wei Xu, Yihui Ren +2
Reliably reconstructing physical fields from sparse sensor data is a challenge that frequently arises in many scientific domains. In practice, the process generating the data often…