Learning constitutive equations of physical components with predefined feasibility conditions. - Ion Matei; Johan de Kleer; Maksym Zhenirovskyy; Alexander Feldman (ACC, 2019)

Inferring Particle Interaction Physical Models and Their Dynamical Properties. - Ion Matei; Christos N. Mavridis; John S. Baras; Maksym Zhenirovskyy (CDC, 2019)

Deep Learning for Control: a non-Reinforcement Learning View. - Ion Matei; Raj Minhas; Maksym Zhenirovskyy; Johan de Kleer; Rahul Rai (ACC, 2020)

Learning physical laws: the case of micron size particles in dielectric fluid. - Ion Matei; Maksym Zhenirovskyy; Johan de Kleer; Christoforos Somarakis; John S. Baras (ACC, 2020)

Improving the Efficiency of Gradient Descent Algorithms Applied to Optimization Problems with Dynamical Constraints. - Ion Matei; Maksym Zhenirovskyy; Johan de Kleer; John Maxwell III (CoRR, 2022)

2D Density Control of Micro-Particles using Kernel Density Estimation. - Ion Matei; Johan de Kleer; Maksym Zhenirovskyy (ACC, 2023)

Sensitivity-Free Gradient Descent Algorithms. - Ion Matei; Maksym Zhenirovskyy; Johan de Kleer; John Maxwell III (J. Mach. Learn. Res., 2023)