most citedTrack Seed Classification with Deep Neural Networks

1 citations · 1 across the 1 of their papers we have counts for

collaborators

6 papers

physics.ins-det20191 cited

Track Seed Classification with Deep Neural Networks

Felix Dietrich

Future upgrades to the LHC will pose considerable challenges for traditional particle track reconstruction methods. We investigate how artificial Neural Networks and Deep Learning…

cs.LG2019

Domain Adaptation with Optimal Transport on the Manifold of SPD matrices

Or Yair, Felix Dietrich, Ronen Talmon +1

In this paper, we address the problem of Domain Adaptation (DA) using Optimal Transport (OT) on Riemannian manifolds. We model the difference between two domains by a diffeomorphis…

physics.acc-ph2019

Status of the undulator-based ILC positron source

Felix Dietrich, Gudrid Moortgat-Pick, Sabine Riemann +2

The design of the positron source for the International Linear Collider (ILC) is still under consideration. The baseline design plans to use the electron beam for the positron prod…

math.OC2018

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…

physics.data-an2018

Manifold Learning for Organizing Unstructured Sets of Process Observations

Felix Dietrich, Mahdi Kooshkbaghi, Erik M. Bollt +1

Data mining is routinely used to organize ensembles of short temporal observations so as to reconstruct useful, low-dimensional realizations of an underlying dynamical system. In t…

physics.acc-ph2018

The ILC positron target cooled by thermal radiation

Sabine Riemann, Felix Dietrich, Gudrid Moortgat-Pick +2

The design of the conversion target for the undulator-based ILC positron source is still under development. One important issue is the cooling of the target. Here, the status of th…