2 papers
eess.SY2026
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…
cs.LG2025
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…