Samuel E. Otto | DeepAI

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Sebastian Peitz
11 publications
Clarence W. Rowley
6 publications
Alberto Padovan
1 publication
Gregory R. Macchio
1 publication

∙ 07/28/2023

Learning Nonlinear Projections for Reduced-Order Modeling of Dynamical Systems using Constrained Autoencoders

Recently developed reduced-order modeling techniques aim to approximate ...

0 Samuel E. Otto, et al. ∙

∙ 09/20/2022

Learning Bilinear Models of Actuated Koopman Generators from Partially-Observed Trajectories

Data-driven models for nonlinear dynamical systems based on approximatin...

0 Samuel E. Otto, et al. ∙

∙ 07/28/2022

Model Reduction for Nonlinear Systems by Balanced Truncation of State and Gradient Covariance

Data-driven reduced-order models often fail to make accurate forecasts o...

0 Samuel E. Otto, et al. ∙

∙ 01/27/2021

Inadequacy of Linear Methods for Minimal Sensor Placement and Feature Selection in Nonlinear Systems; a New Approach Using Secants

Sensor placement and feature selection are critical steps in engineering...

0 Samuel E. Otto, et al. ∙

∙ 05/18/2019

A Discrete Empirical Interpolation Method for Interpretable Immersion and Embedding of Nonlinear Manifolds

Manifold learning techniques seek to discover structure-preserving mappi...

0 Samuel E. Otto, et al. ∙

∙ 12/04/2017

Linearly-Recurrent Autoencoder Networks for Learning Dynamics

This paper describes a method for learning low-dimensional approximation...

0 Samuel E. Otto, et al. ∙

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