3 citations · 3 across the 9 of their papers we have counts for
9 papers
Two Adjoint Perspectives on Fokker-Planck Optimization: A Microscopic-Macroscopic Correspondence
Kathrin Hellmuth, Qin Li, Yunan Yang
The Fokker-Planck equation admits both a macroscopic Eulerian description through probability densities and a microscopic Lagrangian description through stochastic trajectories. Co…
Finding Koopman Invariant Subspaces via Personalized PageRank
Hyukpyo Hong, Qin Li, Matthew J. Colbrook +1
Selecting a finite dictionary of observables whose span is Koopman-invariant is a central challenge in data-driven Koopman operator approximation. We address this problem by exploi…
Optimal drift optimizer for non-convex optimization
Qin Li, Sixu Li, Eitan Tadmor +1
We study a finite-horizon stochastic control criterion for non-convex optimization in which Brownian exploration is balanced against a quadratic control cost. Rather than emphasizi…
Provable imitation learning for control of instability in partially-observed Vlasov--Poisson equations
Xiaofan Xia, Qin Li, Wenlong Mou
We consider the stabilization of Vlasov--Poisson plasma dynamics, a central control problem in nuclear fusion. Our focus is the gap between what an ideal controller would use and w…
Stochastic Modified Equations for Stochastic Gradient Descent in Infinite-Dimensional Hilbert Spaces
Sandra Cerrai, Qin Li, Anjali Nair +1
Inverse problems in scientific computing often require optimization over infinite-dimensional Hilbert spaces. A commonly used solver in such settings is stochastic gradient descent…
Local sensitivity-preserving random data down-sampling for experimental design
Kathrin Hellmuth, Christian Klingenberg, Qin Li
The quality of numerical reconstructions for unknown parameters in inverse problems depends fundamentally on the selection of experimental data. To ensure a robust reconstruction,…
Reconstructing the kinetic chemotaxis kernel using macroscopic data: well-posedness and ill-posedness
Kathrin Hellmuth, Christian Klingenberg, Qin Li +1
Bacterial motion is steered by external stimuli (chemotaxis), and the motion described on the mesoscopic scale is uniquely determined by a parameter that models velocity change…
Kinetic chemotaxis tumbling kernel determined from macroscopic quantities
Kathrin Hellmuth, Christian Klingenberg, Qin Li +1
Chemotaxis is the physical phenomenon that bacteria adjust their motions according to chemical stimulus. A classical model for this phenomenon is a kinetic equation that describes…
On quantifying uncertainties for the linearized BGK kinetic equation
C. Klingenberg, Q. Li, M. Pirner
We consider the linearized BGK equation and want to quantify uncertainties in the case of modelling errors. More specifically, we want to quantify the error produced if the pre-det…