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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...
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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...
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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-...
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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...
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11/09/2020
Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics
Recently developed physics-informed neural network (PINN) has achieved s...
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02/25/2020
Sparsity-promoting algorithms for the discovery of informative Koopman invariant subspaces
Koopman decomposition is a non-linear generalization of eigen decomposit...
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09/16/2019
Particle reconstruction of volumetric particle image velocimetry with strategy of machine learning
Three-dimensional particle reconstruction with limited two-dimensional p...
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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 ...
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05/30/2019
Prediction of Aerodynamic Flow Fields Using Convolutional Neural Networks
An approximation model based on convolutional neural networks (CNNs) is ...
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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 ...
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03/25/2018