activity
20192025
most citedEquivariant neural networks and equivarification

6 citations · 7 across the 6 of their papers we have counts for

collaborators

6 papers

math.NA2025

Dynamical feedback control with operator learning for the Vlasov-Poisson system

Jingcheng Lu, Li Wang, Jeff Calder

To meet the demands of instantaneous control of instabilities over long time horizons in plasma fusion, we design a dynamic feedback control strategy for the Vlasov-Poisson system…

math.NA2025

Controlling instability in the Vlasov-Poisson system through moment-based optimization

Jingcheng Lu, Li Wang, Jeff Calder

Controlling instability in plasma is one of the central challenges in fusion energy research. Among the various sources of instability, kinetic effects play a significant role. In…

cs.LG2024

Attraction-Repulsion Swarming: A Generalized Framework of t-SNE via Force Normalization and Tunable Interactions

Jingcheng Lu, Jeff Calder

We propose a new method for data visualization based on attraction-repulsion swarming (ARS) dynamics, which we call ARS visualization. ARS is a generalized framework that is based…

math.NA2024★ 1 cited

Continuous data assimilation for hydrodynamics: consistent discretization and application to moment recovery

Jingcheng Lu, Kunlun Qi, Li Wang +1

Motivated by the challenge of moment recovery in hydrodynamic approximation in kinetic theory, we propose a data-driven approach for the hydrodynamic models. Inspired by continuous…

math.NA2023

Revisting high-resolution schemes with van-Albada slope limiter

Jingcheng Lu, Eitan Tadmor

Slope limiters play an essential role in maintaining the non-oscillatory behavior of high-resolution methods for nonlinear conservation laws. The family of minmod limiters serves a…

cs.LG2019★ 6 cited

Equivariant neural networks and equivarification

Erkao Bao, Jingcheng Lu, Linqi Song +3

Equivariant neural networks are a class of neural networks designed to preserve symmetries inherent in the data. In this paper, we introduce a general method for modifying a neural…