David Horntrop
Associate Professor, Mathematical Sciences
- david.horntrop@njit.edu
- 973-596-5678
- 627 Cullimore Hall (CULM)
Education
Ph.D.; Princeton University; Applied And Computational Mathematics; 1995
M.A.; Princeton University; Applied And Computational Mathematics; 1992
B.A.; Washington University in St Louis; Mathematics; 1990
B.S.; Washington University in St Louis; Systems Science And Engineering; 1990
Website
2026 Fall Courses
MATH 310 - WORK EXPERIENCE I
MATH 448 - STOCHASTIC SIMULATION
Past Courses
In Progress
Application of Machine Learning to Discrete Interacting Particle Systems
Application of machine learning models to discrete interacting particle systems using recurrent neural networks, graph neural networks, and physics informed models.
Application of machine learning models to discrete interacting particle systems using recurrent neural networks, graph neural networks, and physics informed models.
Density Relaxation in Granular Systems
One of the principal findings in the tapped density relaxation study (that involved both stochastic and deterministic simulations) was the discovery of a dynamical process responsible for the phenomenon, namely, the upward progression of self-organized layers induced by a plane boundary. Indeed, its occurrence in both simulation models suggests the universality of this mechanism in density relaxation which, to our knowledge, had not been previously reported in the literature. An equally striking result was the identification of the existence of critical tap amplitude which optimizes the evolution process. This work has spurred the development of dynamical systems models by my colleague (Prof. D. Blackmore), using a first principals approach, which in turn enabled us to initiate a collaboration with Prof. Tricoche, a computer scientist at Purdue University with expertise in identifying and characterizing dynamically evolving structures in large data sets. <br> Our collaboration has resulted in the publication of several peer-reviewed journal papers, conference papers and presentations.
One of the principal findings in the tapped density relaxation study (that involved both stochastic and deterministic simulations) was the discovery of a dynamical process responsible for the phenomenon, namely, the upward progression of self-organized layers induced by a plane boundary. Indeed, its occurrence in both simulation models suggests the universality of this mechanism in density relaxation which, to our knowledge, had not been previously reported in the literature. An equally striking result was the identification of the existence of critical tap amplitude which optimizes the evolution process. This work has spurred the development of dynamical systems models by my colleague (Prof. D. Blackmore), using a first principals approach, which in turn enabled us to initiate a collaboration with Prof. Tricoche, a computer scientist at Purdue University with expertise in identifying and characterizing dynamically evolving structures in large data sets. <br> Our collaboration has resulted in the publication of several peer-reviewed journal papers, conference papers and presentations.
Journal Article
Conference Proceeding
Chapter