
Structurepreserving Sparse Identification of Nonlinear Dynamics for Datadriven Modeling
Discovery of dynamical systems from data forms the foundation for datad...
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Probabilistic partition of unity networks: clustering based deep approximation
Partition of unity networks (POUNets) have been shown capable of realiz...
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Machine learning structure preserving brackets for forecasting irreversible processes
Forecasting of timeseries data requires imposition of inductive biases ...
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On Surrogate Learning for Linear Stability Assessment of NavierStokes Equations with Stochastic Viscosity
We study linear stability of solutions to the Navier–Stokes equations wi...
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Partition of unity networks: deep hpapproximation
Approximation theorists have established bestinclass optimal approxima...
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DPM: A Novel Training Method for PhysicsInformed Neural Networks in Extrapolation
We present a method for learning dynamics of complex physical processes ...
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Parameterized Neural Ordinary Differential Equations: Applications to Computational Physics Problems
This work proposes an extension of neural ordinary differential equation...
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Alternating Energy Minimization Methods for Multiterm Matrix Equations
We develop computational methods for approximating the solution of a lin...
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Solving LargeScale 01 Knapsack Problems and its Application to Point Cloud Resampling
01 knapsack is of fundamental importance in computer science, business,...
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Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders
Nearly all modelreduction techniques project the governing equations on...
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Kookjin Lee
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