Shaowu Pan | DeepAI

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J. Nathan Kutz
64 publications
Steven L. Brunton
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Jinjun Wang
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Karthik Duraisamy
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Qi Gao
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Weiqi Ji
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Sili Deng
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Eurika Kaiser
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Runjie Wei
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Saakaar Bhatnagar
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Brian M. de Silva
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∙ 06/22/2023

PyKoopman: A Python Package for Data-Driven Approximation of the Koopman Operator

PyKoopman is a Python package for the data-driven approximation of the K...

0 Shaowu Pan, et al. ∙

∙ 04/24/2023

On the lifting and reconstruction of dynamical systems with multiple attractors

The Koopman operator provides a linear perspective on non-linear dynamic...

0 Shaowu Pan, et al. ∙

∙ 04/07/2022

Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal data

High-dimensional spatio-temporal dynamics can often be encoded in a low-...

124 Shaowu Pan, et al. ∙

∙ 09/14/2021

Non-linear Independent Dual System (NIDS) for Discretization-independent Surrogate Modeling over Complex Geometries

Numerical solutions of partial differential equations (PDEs) require exp...

11 James Duvall, et al. ∙

∙ 11/09/2020

Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics

Recently developed physics-informed neural network (PINN) has achieved s...

1 Weiqi Ji, et al. ∙

∙ 02/25/2020

Sparsity-promoting algorithms for the discovery of informative Koopman invariant subspaces

Koopman decomposition is a non-linear generalization of eigen decomposit...

12 Shaowu Pan, et al. ∙

∙ 09/16/2019

Particle reconstruction of volumetric particle image velocimetry with strategy of machine learning

Three-dimensional particle reconstruction with limited two-dimensional p...

1 Qi Gao, et al. ∙

∙ 06/09/2019

Physics-Informed Probabilistic Learning of Linear Embeddings of Non-linear Dynamics With Guaranteed Stability

The Koopman operator has emerged as a powerful tool for the analysis of ...

1 Shaowu Pan, et al. ∙

∙ 05/30/2019

Prediction of Aerodynamic Flow Fields Using Convolutional Neural Networks

An approximation model based on convolutional neural networks (CNNs) is ...

1 Yaser Afshar, et al. ∙

∙ 05/31/2018

Long-time predictive modeling of nonlinear dynamical systems using neural networks

We study the use of feedforward neural networks (FNN) to develop models ...

1 Shaowu Pan, et al. ∙

∙ 03/25/2018

Data-driven Discovery of Closure Models

Derivation of reduced order representations of dynamical systems require...

1 Shaowu Pan, et al. ∙

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