72 citations · 118 across the 7 of their papers we have counts for
7 papers
Data-driven Discovery of Chemotactic Migration of Bacteria via Machine Learning
Yorgos M. Psarellis, Seungjoon Lee, Tapomoy Bhattacharjee +3
E. coli chemotactic motion in the presence of a chemoattractant field has been extensively studied using wet laboratory experiments, stochastic computational models as well as part…
Learning black- and gray-box chemotactic PDEs/closures from agent based Monte Carlo simulation data
Seungjoon Lee, Yorgos M. Psarellis, Constantinos I. Siettos +1
We propose a machine learning framework for the data-driven discovery of macroscopic chemotactic Partial Differential Equations (PDEs) -- and the closures that lead to them -- from…
Blended Dynamics Approach to Distributed Optimization: Sum Convexity and Convergence Rate
Seungjoon Lee, Hyungbo Shim
This paper studies the application of the blended dynamics approach towards distributed optimization problem where the global cost function is given by a sum of local cost function…
Coarse-scale PDEs from fine-scale observations via machine learning
Seungjoon Lee, Mahdi Kooshkbaghi, Konstantinos Spiliotis +2
Complex spatiotemporal dynamics of physicochemical processes are often modeled at a microscopic level (through e.g. atomistic, agent-based or lattice models) based on first princip…
Distributed Algorithm for Economic Dispatch Problem with Separable Losses
Seungjoon Lee, Hyungbo Shim
Economic dispatch problem for a networked power system has been considered. The objective is to minimize the total generation cost while meeting the overall supply-demand balance a…
Linking Gaussian Process regression with data-driven manifold embeddings for nonlinear data fusion
Seungjoon Lee, Felix Dietrich, George E. Karniadakis +1
In statistical modeling with Gaussian Process regression, it has been shown that combining (few) high-fidelity data with (many) low-fidelity data can enhance prediction accuracy, c…
Some manifold learning considerations towards explicit model predictive control
Robert J. Lovelett, Felix Dietrich, Seungjoon Lee +1
Model predictive control (MPC) is a de facto standard control algorithm across the process industries. There remain, however, applications where MPC is impractical because an optim…