Promotionen der Fakultät für Informatik im Jahr 2026

19.01.2026

Farahani, Aida
"Exploring Deep Learning Approaches for 3D Deformation: Toward Finite Element Method Distillation"
Promotion zum Dr. Ing.

Abstract:

The thesis introduces neural frameworks for modeling 3D deformations with the goal of enhancing the predictive accuracy and computational efficiency of traditional Finite Element Method (FEM) simulations. It explores two complementary approaches: a single-step deformation model that employs implicit neural representations and signed distance fields to approximate FEM-based deformations up to 400× faster than conventional simulations, and a multi-step deformation model that formulates deformation as a sequential decision-making process within a deep reinforcement learning framework. Supported by two custom datasets, DefBeam and DefCube, the study demonstrates that AI-driven methods can effectively complement FEM by accelerating simulations and improving accuracy in applications such as material design, virtual prototyping, and industrial forming.

16.01.2026

Dadgar, Seyed Amin
"Application-Independent Recognition of 3D Hand Gestures using Synthetic Data"
Promotion zum Dr. Ing.

Abstract:

This dissertation introduces several novel approaches for an overarching context of vision-based application-independent 3D hand gesture recognition using a single RGB image as input. The focus is to leverage synthetic data and to address key challenges in hand detection, segmentation, pose estimation, and dynamic gesture modeling. It proposes a fully hierarchical, layered-based, and flexible hand data structure with a comprehensive one-dimensional PoseDescriptor grounded in finger kinematics to efficiently represent and formulate diverse postures, reducing high degrees of freedom while enabling classification of diverse hand postures and expensive searches (e.g., exhaustive search for 3D pose estimation). It introduces a comprehensive Markovian system that incorporates evolutionary topological constraints to enrich the model of temporal relationships between postures and to enhance the real-time performance and accuracy of the 3D Pose estimation of fingers and hand movement across varied gestures. Finally, it proposes training methodologies for deep neural networks using simplistic synthetic image generation, exploiting the invariancy concept, and mitigating premature saturation to achieve robust results in real-world scenarios with convolutional networks like YOLO for detection and Mask-RCNN for segmentation. The results of this dissertation represent a selection of the successful research undertaken during the PhD period and also include several ideas that light promising directions for future work in this domain.

12.01.2026

Markert, Daniel
"On the Design of Synchronous Traffic Protocols for Intelligent Intersections"
Promotion zum Dr. Ing.

Abstract:

Summary of Findings 
Confining strategies for intelligent intersections to a specific type of traffic or infrastructure greatly restricts their applicability in the real world. To alleviate this, this thesis proposes several techniques with the goal of bridging these gaps and making these protocols applicable to a wider range of settings. Specifically, this thesis proposed a probabilistic modeling of communication reliability to reduce deterministic pessimism, as well as three traffic protocols: i) SV-LTR to increase space efficiency, ii) PB-LTR to apply the concept of platooning to intelligent intersections and iii) FleXS-TP to increase flexibility towards traffic composition.

Reducing Deterministic
Pessimism Ensuring communication reliability between vehicles and the corresponding road side unit (RSU) requires knowledge of the maximum number of vehicles in the system to assess interfer­ence. By using a realistic, probabilistic vehicle length distribution instead of assuming langest possible vehicle allows for a reduction in deterministic pessimism and the following overdesign by using stepwise, probabilistic warst cases. 

lnfrastructure-Agnostic
Design Treating extraordinarily large vehicles as exception instead of the norm leads to an uncoupling of the traffic protocols sector size S from the actual traffic, as introduced in the space-efficient traffic protocol SV-LTR and also used later in PB-LTR and FleXS-TP. Instead, the existing infrastructure now provides the sector size S, allowing for infrastructure-agnostic design where the traffic protocol can be applied to any given infrastructure with the existing geometry pa­rameterizing the protocol, alleviating the need for great modifications of existing intersections, while providing competitive or even greater throughput. 

Platooning for Well-Behaved Traffic 
The traffic protocol PB-LTR extends SV-LTR's single-vehicle maneuvers to platoons consec­utively performing the same maneuver, enabling shorter inter-vehicle distances and reducing downtime from maneuver transitions. To ensure all vehicles eventually cross the intersection, a maximum blocking time regularly enforces maneuver changes, guaranteeing fairness across all directions. Under well-behaved traffic, PB-LTR can further increase throughput. 

Increasing Flexibility towards Traffic Composition 
By maintaining a synchronous strategy at heart, but scheduling vehicles on a vehicle-by-vehicle basis while still allowing for situational synchronicity, the traffic protocol FleXS-TP can harness both the benefits of synchronous traffic protocols for well-behaved traffic, as well as the flexibil­ity of asynchronous traffic protocols under randomized traffic, achieving substantial throughput in both settings - combining the best of asynchronous and synchronous strategies.