Sebastian Herzog

Orcid: 0000-0001-6527-5396

According to our database1, Sebastian Herzog authored at least 16 papers between 2014 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
proPTV: A probability-based particle tracking velocimetry framework.
J. Comput. Phys., 2024

2022
Finding Optimal Paths Using Networks Without Learning - Unifying Classical Approaches.
IEEE Trans. Neural Networks Learn. Syst., 2022

Application of neural ordinary differential equations to the prediction of multi-agent systems.
Artif. Life Robotics, 2022

2021
Data-driven modelling of non-linear systems by means of artificial neural network hybrids.
PhD thesis, 2021

A Probabilistic Particle Tracking Framework for Guided and Brownian Motion Systems with High Particle Densities.
SN Comput. Sci., 2021

2020
Evolving artificial neural networks with feedback.
Neural Networks, 2020

One-Shot Multi-Path Planning Using Fully Convolutional Networks in a Comparison to Other Algorithms.
Frontiers Neurorobotics, 2020

Reconstructing Complex Cardiac Excitation Waves From Incomplete Data Using Echo State Networks and Convolutional Autoencoders.
Frontiers Appl. Math. Stat., 2020

One-shot path planning for multi-agent systems using fully convolutional neural network.
CoRR, 2020

Generation of Paths in a Maze using a Deep Network without Learning.
CoRR, 2020

One-Shot Multi-Path Planning for Robotic Applications Using Fully Convolutional Networks.
Proceedings of the 2020 IEEE International Conference on Robotics and Automation, 2020

2018
Data-Driven Modeling and Prediction of Complex Spatio-Temporal Dynamics in Excitable Media.
Frontiers Appl. Math. Stat., 2018

2017
Generation of movements with boundary conditions based on optimal control theory.
Robotics Auton. Syst., 2017

Transfer entropy-based feedback improves performance in artificial neural networks.
CoRR, 2017

2016
Optimal trajectory generation for generalization of discrete movements with boundary conditions.
Proceedings of the 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2016

2014
Quantum Coupled Mutation Finder: Predicting functionally or structurally important sites in proteins using quantum Jensen-Shannon divergence and CUDA programming.
BMC Bioinform., 2014


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