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cs.LG2026
Data-driven stochastic reduced-order modeling of parametrized dynamical systems
Andrew F. Ilersich, Kevin Course, Prasanth B. Nair
Modeling complex dynamical systems under varying conditions is computationally intensive, often rendering high-fidelity simulations intractable. Although reduced-order models (ROMs…
cs.LG2023
Amortized Reparametrization: Efficient and Scalable Variational Inference for Latent SDEs
Kevin Course, Prasanth B. Nair
We consider the problem of inferring latent stochastic differential equations (SDEs) with a time and memory cost that scales independently with the amount of data, the total length…
cs.LG2021
Weak Form Generalized Hamiltonian Learning
Kevin L. Course, Trefor W. Evans, Prasanth B. Nair
We present a method for learning generalized Hamiltonian decompositions of ordinary differential equations given a set of noisy time series measurements. Our method simultaneously…