20 citations · 61 across the 10 of their papers we have counts for
11 papers
PySHRED: A Python package for SHallow REcurrent Decoding for sparse sensing, model reduction and scientific discovery
David Ye, Jan Williams, Mars Gao +4
SHallow REcurrent Decoders (SHRED) provide a deep learning strategy for modeling high-dimensional dynamical systems and/or spatiotemporal data from dynamical system snapshot observ…
Sparse identification of nonlinear dynamics and Koopman operators with Shallow Recurrent Decoder Networks
Mars Liyao Gao, Jan P. Williams, J. Nathan Kutz
Modeling real-world spatio-temporal data is exceptionally difficult due to inherent high dimensionality, measurement noise, partial observations, and often expensive data collectio…
Convergence of uncertainty estimates in Ensemble and Bayesian sparse model discovery
L. Mars Gao, Urban Fasel, Steven L. Brunton +1
Sparse model identification enables nonlinear dynamical system discovery from data. However, the control of false discoveries for sparse model identification is challenging, especi…
Bayesian autoencoders for data-driven discovery of coordinates, governing equations and fundamental constants
L. Mars Gao, J. Nathan Kutz
Recent progress in autoencoder-based sparse identification of nonlinear dynamics (SINDy) under constraints allows joint discoveries of governing equations and latent coord…
On Optimal Early Stopping: Over-informative versus Under-informative Parametrization
Ruoqi Shen, Liyao Gao, Yi-An Ma
Early stopping is a simple and widely used method to prevent over-training neural networks. We develop theoretical results to reveal the relationship between the optimal early stop…
Deformation Robust Roto-Scale-Translation Equivariant CNNs
Liyao Gao, Guang Lin, Wei Zhu
Incorporating group symmetry directly into the learning process has proved to be an effective guideline for model design. By producing features that are guaranteed to transform cov…
Quantifying Uncertainty in Deep Spatiotemporal Forecasting
Dongxia Wu, Liyao Gao, Xinyue Xiong +4
Deep learning is gaining increasing popularity for spatiotemporal forecasting. However, prior works have mostly focused on point estimates without quantifying the uncertainty of th…
DeepGLEAM: A hybrid mechanistic and deep learning model for COVID-19 forecasting
Dongxia Wu, Liyao Gao, Xinyue Xiong +4
We introduce DeepGLEAM, a hybrid model for COVID-19 forecasting. DeepGLEAM combines a mechanistic stochastic simulation model GLEAM with deep learning. It uses deep learning to lea…
Non-convex Learning via Replica Exchange Stochastic Gradient MCMC
Wei Deng, Qi Feng, Liyao Gao +2
Replica exchange Monte Carlo (reMC), also known as parallel tempering, is an important technique for accelerating the convergence of the conventional Markov Chain Monte Carlo (MCMC…
RotEqNet: Rotation-Equivariant Network for Fluid Systems with Symmetric High-Order Tensors
Liyao Gao, Yifan Du, Hongshan Li +1
In the recent application of scientific modeling, machine learning models are largely applied to facilitate computational simulations of fluid systems. Rotation symmetry is a gener…
Cortex Neural Network: learning with Neural Network groups
Liyao Gao
Neural Network has been successfully applied to many real-world problems, such as image recognition and machine translation. However, for the current architecture of neural network…