5 papers
State Forecasting in an Estimation Framework with Surrogate Sensor Modeling
Sriram Narayanan, Mohamed Naveed Gul Mohamed, Ishan Paranjape +3
In recent years, computational power and data availability breakthroughs have revolutionized our ability to analyze complex physical systems through the inverse problem approach. D…
Parametric Interpolation of Dynamic Mode Decomposition for Predicting Nonlinear Systems
Ananda Chakrabarti, Haitham H. Saleh, Indranil Nayak +3
We present parameter-interpolated dynamic mode decomposition (piDMD), a parametric reduced-order modeling framework that embeds known parameter-affine structure directly into the D…
Temporally Consistent Koopman Autoencoders for Forecasting Dynamical Systems
Indranil Nayak, Ananda Chakrabarty, Mrinal Kumar +2
Absence of sufficiently high-quality data often poses a key challenge in data-driven modeling of high-dimensional spatio-temporal dynamical systems. Koopman Autoencoders (KAEs) har…
Temporally-Consistent Bilinearly Recurrent Autoencoders for Control Systems
Ananda Chakrabarti, Indranil Nayak, Debdipta Goswami
This paper introduces the temporally-consistent bilinearly recurrent autoencoder (tcBLRAN), a Koopman operator based neural network architecture for modeling a control-affine nonli…
Time-delayed Dynamic Mode Decomposition for families of periodic trajectories in Cislunar Space
Sriram Narayanan, Mohamed Naveed Gul Mohamed, Indranil Nayak +2
In recent years, the development of the Lunar Gateway and Artemis missions has renewed interest in lunar exploration, including both manned and unmanned missions. This interest nec…