Sven Tomforde

Prof.

Christian-Albrechts-Universität zu Kiel

Germany

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Professor Sven Tomforde is a faculty member at Christian-Albrechts-Universität zu Kiel, Germany. His research focuses on robotics, artificial intelligence in scientific research, microcrack detection, and environmental sustainability. Recent publications include studies on robotic manipulators, real-time condition monitoring in SASO systems, and enhancing soil health through innovative agricultural techniques.

Articles (16)

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2025

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2025

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2025

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2025

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2025

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2024

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2023

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2023

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2023

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2023

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2022

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2022

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2022

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2022

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2021

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2020

Collaborators (4)

Martin Braun

GERMANY

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Muhammad Shafiq

king saud university

SAUDI ARABIA

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Sören Christensen

Professor

Christian-Albrechts-Universität zu Kiel

GERMANY

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Jakob Nazarenus

Kiel University

GERMANY

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Sven Tomforde

Research Interests

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Articles (16)

Occlusion Avoidance for Robotic Manipulators Using Rigid Gaussian Splatting

The Future Is Organic: A Deep Dive into Techniques and Applications for Real-Time Condition Monitoring in SASO Systems—A Systematic Review

A Helping Hand: A Survey About AI-Driven Experimental Design for Accelerating Scientific Research

MicrocrackAttentionNext: Advancing Microcrack Detection in Wave Field Analysis Using Deep Neural Networks Through Feature Visualization

Regression models for the prediction of the influence of magnesium ions on primary endothelial cell (HUVEC) proliferation and migration

HUGO – Highlighting Unseen Grid Options: Combining deep reinforcement learning with a heuristic target topology approach

Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agents

Optimizing Sustainable Phytoextraction of Lead from Contaminated Soil Using Response Surface Methodology (RSM) and Artificial Neural Network (ANN)

Enhanced lead phytoextraction and soil health restoration through exogenous supply of organic ligands: Geochemical modeling”

Erratum to: Reinforcement learning as a basis for cross domain fusion of heterogeneous data

Reinforcement learning as a basis for cross domain fusion of heterogeneous data

Reinforcement learning as a basis for cross domain fusion of heterogeneous data

Adaptive Approaches for Tidal-Flow Lanes in Urban-Road Networks

The Vision of Self-Management in Cognitive Organic Power Distribution Systems

On the stability of (self-)adaptive behaviour in continuously changing environments: A quantification approach

Introduction to the Special Issue with Selected Papers of The International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS) 2020

Collaborators (4)

Martin Braun

Muhammad Shafiq

Sören Christensen

Jakob Nazarenus

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