11 citations · 14 across the 5 of their papers we have counts for
6 papers
Stochastic Operator Inference for reduced-order modeling of capillary wave turbulence using experimental measurements
Hyeonghun Kim, Lei Zhang, James Friend +1
Modeling complex physical phenomena directly from experimental data poses fundamental challenges: measurement devices introduce noise and biases into the data, the governing equati…
Quadratization of Autonomous Partial Differential Equations: Algorithmic Solutions
Albani Olivieri, Gleb Pogudin, Boris Kramer
Quadratization for partial differential equations (PDEs) is a process that formally transforms a PDE with a nonquadratic right-hand side into a quadratic form by introducing auxili…
Parametric Operator Inference to Simulate the Purging Process in Semiconductor Manufacturing
Seunghyon Kang, Hyeonghun Kim, Boris Kramer
This work presents the application of parametric Operator Inference (OpInf) -- a nonintrusive reduced-order modeling (ROM) technique that learns a low-dimensional representation of…
Physically consistent predictive reduced-order modeling by enhancing Operator Inference with state constraints
Hyeonghun Kim, Boris Kramer
Numerical simulations of complex multiphysics systems, such as char combustion considered herein, yield numerous state variables that inherently exhibit physical constraints. This…
Data-Driven Reduced-Order Models for Port-Hamiltonian Systems with Operator Inference
Yuwei Geng, Lili Ju, Boris Kramer +1
Hamiltonian operator inference has been developed in [Sharma, H., Wang, Z., Kramer, B., Physica D: Nonlinear Phenomena, 431, p.133122, 2022] to learn structure-preserving reduced-o…
Gradient Preserving Operator Inference: Data-Driven Reduced-Order Models for Equations with Gradient Structure
Yuwei Geng, Jasdeep Singh, Lili Ju +2
Hamiltonian Operator Inference has been introduced in [Sharma, H., Wang, Z., Kramer, B., Physica D: Nonlinear Phenomena, 431, p.133122, 2022] to learn structure-preserving reduced-…