machine learning

The presentation series “Train your engineering network” on diverse topics of Machine Learning addresses all interested persons at TUHH, from MLE partners as well as from the Hamburg region in general and aims at promoting the exchange of information and knowledge between these persons as well as their networking in a relaxed atmosphere. Thereby, the machine learning activities within MLE, TUHH and in the wider environment shall be made more visible, cooperations shall be promoted and also interested students shall be given an insight.

Niklas Dieckow - Data-driven methods for the Maxey-Riley equations

The Maxey-Riley equations (MRE) describe the motion of a small inertial particle suspended in a fluid flow. They are a system of implicit integro-differential equations with a singular kernel. Exact solution methods require the evaluation of an integral over the entire particle history in each time step, causing the computation time to grow quadratically in the number of steps. In this talk, data-driven methods such as SINDy (Sparse Identification of Nonlinear Dynamics) are discussed and employed to obtain approximations of the MRE that do not contain an integral term and are therefore easier to solve.

Lectures will be held online via Zoom on Mondays starting at 16:00 in the winter semester 2023 in English. General zoom link for all lectures: Link

The presentation series “Train your engineering network” on diverse topics of Machine Learning addresses all interested persons at TUHH, from MLE partners as well as from the Hamburg region in general and aims at promoting the exchange of information and knowledge between these persons as well as their networking in a relaxed atmosphere. Thereby, the machine learning activities within MLE, TUHH and in the wider environment shall be made more visible, cooperations shall be promoted and also interested students shall be given an insight.

Johanna Gleichauf - Sensor Fusion for the Robust Detection of Facial Regions of Neonates Using Neural Networks

All talks will be streamed via Zoom using https://tuhh.zoom.us/j/85203195489?pwd=K21saVMvZHc0d2NoNHd2bDZ6TmdDUT09
Meeting-ID: 852 0319 5489
Code: 827469

The presentation series “Train your engineering network” on diverse topics of Machine Learning addresses all interested persons at TUHH, from MLE partners as well as from the Hamburg region in general and aims at promoting the exchange of information and knowledge between these persons as well as their networking in a relaxed atmosphere. Thereby, the machine learning activities within MLE, TUHH and in the wider environment shall be made more visible, cooperations shall be promoted and also interested students shall be given an insight.

Philip Suskin - Development of a Conversational Interface Based on Institution-Specific Documentation Through LLM Finetuning

All talks will be streamed via Zoom using https://tuhh.zoom.us/j/85203195489?pwd=K21saVMvZHc0d2NoNHd2bDZ6TmdDUT09
Meeting-ID: 852 0319 5489
Code: 827469

The presentation series “Train your engineering network” on diverse topics of Machine Learning addresses all interested persons at TUHH, from MLE partners as well as from the Hamburg region in general and aims at promoting the exchange of information and knowledge between these persons as well as their networking in a relaxed atmosphere. Thereby, the machine learning activities within MLE, TUHH and in the wider environment shall be made more visible, cooperations shall be promoted and also interested students shall be given an insight.

Emin Nakilcioglu - Parameter Efficient Fine Tuning for a Domain-Specific Automatic Speech Recognition

With the introduction of early pre-trained language models such as Google’s BERT and various early GPT models, we have seen an ever-increasing excitement and interest in foundation models. To leverage existing pre-trained foundation models and adapt them to specific tasks or domains, these models need to be fine-tuned using domain-specific data. However, fine-tuning can be quite resource-intensive and costly as millions of  parameters will be modified as part of training. PEFT is a technique designed to fine-tune models while minimizing the need for extensive resources and cost. It achieves this efficiency by freezing some of the layers of the pre-trained model and only fine-tuning the last few layers that are specific to the downstream task. With the help of PEFT, we can achieve a balance between retaining valuable knowledge from the pre-trained model and adapting it effectively to the downstream task with fewer parameters.

Lectures will be held online via Zoom on Mondays starting at 16:00 in the winter semester 2023 in English. General zoom link for all lectures: Link

The presentation series “Train your engineering network” on diverse topics of Machine Learning addresses all interested persons at TUHH, from MLE partners as well as from the Hamburg region in general and aims at promoting the exchange of information and knowledge between these persons as well as their networking in a relaxed atmosphere. Thereby, the machine learning activities within MLE, TUHH and in the wider environment shall be made more visible, cooperations shall be promoted and also interested students shall be given an insight.

Alexander Itin - AI for engineering and science: selected use cases

Its going to be a little bit chaotic talk with many use cases.  My scientific background is dynamical systems and condensed matter physics.  I switched from academic to industrial research at some point, joining Bosch Research, and did many interesting projects there. I then gradually returned back to academy. I will briefly discuss applications of AI in engineering, and in a more detail in science: how they are different and what have in common.  Such use cases as virtual sensors, synthetic FIB/SEM data generation, anomaly detection in manufacturing, etc, will be briefly reviewed (engineering). I will consider in a little bit more detail inverse design of photonic structures using generative AI, predictive physics-informed models, acceleration of solvers using AI  (science).

Lectures will be held online via Zoom on Mondays starting at 16:00 in the winter semester 2023 in English. General zoom link for all lectures: Link

The presentation series “Train your engineering network” on diverse topics of Machine Learning addresses all interested persons at TUHH, from MLE partners as well as from the Hamburg region in general and aims at promoting the exchange of information and knowledge between these persons as well as their networking in a relaxed atmosphere. Thereby, the machine learning activities within MLE, TUHH and in the wider environment shall be made more visible, cooperations shall be promoted and also interested students shall be given an insight.

Niklas Dieckow

All talks will be streamed via Zoom using https://tuhh.zoom.us/j/85203195489?pwd=K21saVMvZHc0d2NoNHd2bDZ6TmdDUT09
Meeting-ID: 852 0319 5489
Code: 827469

The presentation series “Train your engineering network” on diverse topics of Machine Learning addresses all interested persons at TUHH, from MLE partners as well as from the Hamburg region in general and aims at promoting the exchange of information and knowledge between these persons as well as their networking in a relaxed atmosphere. Thereby, the machine learning activities within MLE, TUHH and in the wider environment shall be made more visible, cooperations shall be promoted and also interested students shall be given an insight.

Nathanael Winter

Following the successful application of machine learning methods in order to predict brake squeal as a classification task, this contribution addresses the transfer of those methods on to particle emission data, in order to correctly predict brake particle emissions as a regression task. First results proving the transferability of those methods will be presented.

All talks will be streamed via Zoom using https://tuhh.zoom.us/j/85203195489?pwd=K21saVMvZHc0d2NoNHd2bDZ6TmdDUT09
Meeting-ID: 852 0319 5489
Code: 827469

The presentation series “Train your engineering network” on diverse topics of Machine Learning addresses all interested persons at TUHH, from MLE partners as well as from the Hamburg region in general and aims at promoting the exchange of information and knowledge between these persons as well as their networking in a relaxed atmosphere. Thereby, the machine learning activities within MLE, TUHH and in the wider environment shall be made more visible, cooperations shall be promoted and also interested students shall be given an insight.

Robert Kräuter - Development of a black-box soft sensor for a fluidization process

Solids water content is an important particle property in many applications of process engineering. Its importance on the quality of pharmaceutical formulations makes an in-line measurement of the water content especially desirable in fluidization processes. However, currently available measurement techniques are difficult to calibrate and scarcely applicable in real fluidized beds. A promising strategy for in-line monitoring of the water content is thus soft sensing, a method that expresses the targeted quantity as a correlation of other more reliable measurements. In this talk, we present the development of such a soft sensor using various black-box models. Our focus lies on strategies to reduce overfitting through feature engineering and hyperparameter tuning. These models are designed for processing real experimental data from a turbulent process, addressing challenges in data filtering, undersampling, outlier detection, and uncertainty propagation.

Lectures will be held online via Zoom on Mondays starting at 16:00 in the winter semester 2023 in English. General zoom link for all lectures: Link

The presentation series “Train your engineering network” on diverse topics of Machine Learning addresses all interested persons at TUHH, from MLE partners as well as from the Hamburg region in general and aims at promoting the exchange of information and knowledge between these persons as well as their networking in a relaxed atmosphere. Thereby, the machine learning activities within MLE, TUHH and in the wider environment shall be made more visible, cooperations shall be promoted and also interested students shall be given an insight.

Maximilian Kiener

In this talk, I explore the role of ethics in the development of AI and advanced machine learning. I argue that ethics is deeply integrated into powerful AI systems so that one cannot easily remove it without serious impairment of other aspects of the system’s intelligence and problem-solving capacities. On this basis, I develop a novel and more radical framework for ethics by design.

All talks will be streamed via Zoom using https://tuhh.zoom.us/j/85203195489?pwd=K21saVMvZHc0d2NoNHd2bDZ6TmdDUT09
Meeting-ID: 852 0319 5489
Code: 827469

The presentation series “Train your engineering network” on diverse topics of Machine Learning addresses all interested persons at TUHH, from MLE partners as well as from the Hamburg region in general and aims at promoting the exchange of information and knowledge between these persons as well as their networking in a relaxed atmosphere. Thereby, the machine learning activities within MLE, TUHH and in the wider environment shall be made more visible, cooperations shall be promoted and also interested students shall be given an insight.

Yahya Saleh - Flow-induced bases and application to quantum molecular physics

In analogy to the use of normalizing flows to augment the expressivity of base probability distributions, I propose to augment the expressivity of bases of Hilbert spaces via composition with normalizing flows. I show that the resulting sequences are also bases of the Hilbert space under sufficient and necessary conditions on the flow. This lays a foundation for a theory of spectral learning, a nonlinear extension of spectral methods for solving differential equations. As an application I solve the vibrational molecular Schrödinger equation. The proposed numerical scheme results in several orders of magnitude increased accuracy over the use of standard spectral methods. 

Lectures will be held online via Zoom on Mondays starting at 16:00 in the winter semester 2023 in English. General zoom link for all lectures: Link

The presentation series “Train your engineering network” on diverse topics of Machine Learning addresses all interested persons at TUHH, from MLE partners as well as from the Hamburg region in general and aims at promoting the exchange of information and knowledge between these persons as well as their networking in a relaxed atmosphere. Thereby, the machine learning activities within MLE, TUHH and in the wider environment shall be made more visible, cooperations shall be promoted and also interested students shall be given an insight.

Moritz Braun - Generalizability and explainability of machine learning models for fatigue strength prediction of welded joints

Lectures will be held online via Zoom on Mondays starting at 16:00 in the winter semester 2023 in English. General zoom link for all lectures: Link

The presentation series “Train your engineering network” on diverse topics of Machine Learning addresses all interested persons at TUHH, from MLE partners as well as from the Hamburg region in general and aims at promoting the exchange of information and knowledge between these persons as well as their networking in a relaxed atmosphere. Thereby, the machine learning activities within MLE, TUHH and in the wider environment shall be made more visible, cooperations shall be promoted and also interested students shall be given an insight.

Marius Tacke

All talks will be streamed via Zoom using https://tuhh.zoom.us/j/85203195489?pwd=K21saVMvZHc0d2NoNHd2bDZ6TmdDUT09
Meeting-ID: 852 0319 5489
Code: 827469

The presentation series “Train your engineering network” on diverse topics of Machine Learning addresses all interested persons at TUHH, from MLE partners as well as from the Hamburg region in general and aims at promoting the exchange of information and knowledge between these persons as well as their networking in a relaxed atmosphere. Thereby, the machine learning activities within MLE, TUHH and in the wider environment shall be made more visible, cooperations shall be promoted and also interested students shall be given an insight.

Sören Laue

Most problems from classical machine learning can be cast as an optimization problem. I will present GENO (GENeric Optimization), a framework that lets the user specify a constrained or unconstrained optimization problem in an easy-to-read modeling language. GENO then generates a solver that can solve this class of optimization problems. The generated solver is usually as fast as hand-written, problem-specific, and well-engineered solvers. Often the solvers generated by GENO are faster by a large margin compared to recently developed solvers that are tailored to a specific problem class. I will dig into some of the algorithmic details, e.g., computing derivatives of matrix and tensor expressions, the optimization methods used in GENO, and their implementation in Python.

All talks will be streamed via Zoom using https://tuhh.zoom.us/j/85203195489?pwd=K21saVMvZHc0d2NoNHd2bDZ6TmdDUT09
Meeting-ID: 852 0319 5489
Code: 827469

The presentation series “Train your engineering network” on diverse topics of Machine Learning addresses all interested persons at TUHH, from MLE partners as well as from the Hamburg region in general and aims at promoting the exchange of information and knowledge between these persons as well as their networking in a relaxed atmosphere. Thereby, the machine learning activities within MLE, TUHH and in the wider environment shall be made more visible, cooperations shall be promoted and also interested students shall be given an insight.

Frank Röder - Hindsight Instruction Grounding in Reinforcement Learning

This presentation addresses the challenge of sample inefficiency in robotic reinforcement learning with sparse rewards and natural language goal representations. We introduce a mechanism for hindsight instruction replay, leveraging expert feedback, and a seq2seq model for generating linguistic hindsight instructions. Remarkably, our findings demonstrate that self-supervised language generation, where the agent autonomously generates linguistic instructions, significantly enhances learning performance. These results underscore the promising potential of hindsight instruction grounding in reinforcement learning for robotics.

Lectures will be held online via Zoom on Mondays starting at 16:00 in the winter semester 2023 in English. General zoom link for all lectures: Link

The presentation series “Train your engineering network” on diverse topics of Machine Learning addresses all interested persons at TUHH, from MLE partners as well as from the Hamburg region in general and aims at promoting the exchange of information and knowledge between these persons as well as their networking in a relaxed atmosphere. Thereby, the machine learning activities within MLE, TUHH and in the wider environment shall be made more visible, cooperations shall be promoted and also interested students shall be given an insight.

Lars Stietz - Machine Learning in High Energy Physics

Lectures will be held online via Zoom on Mondays starting at 16:00 in the winter semester 2023 in English. General zoom link for all lectures: Link

The presentation series “Train your engineering network” on diverse topics of Machine Learning addresses all interested persons at TUHH, from MLE partners as well as from the Hamburg region in general and aims at promoting the exchange of information and knowledge between these persons as well as their networking in a relaxed atmosphere. Thereby, the machine learning activities within MLE, TUHH and in the wider environment shall be made more visible, cooperations shall be promoted and also interested students shall be given an insight.

Ana Almeida - Multivariate Time series: Data processing, Imputation and Forecasting

Data is a valuable tool for decision-makers, helping them make informed decisions. We can find multivariate time series in several contexts, such as finances, smart cities, and health. This type of data can bring additional challenges. This presentation will discuss the key concepts and techniques involved in working with multivariate time series data. Specifically, we will focus on the steps of data processing, imputation, and forecasting. 

Lectures will be held online via Zoom on Mondays starting at 16:00 in the winter semester 2023 in English. General zoom link for all lectures: Link

The presentation series “Train your engineering network” on diverse topics of Machine Learning addresses all interested persons at TUHH, from MLE partners as well as from the Hamburg region in general and aims at promoting the exchange of information and knowledge between these persons as well as their networking in a relaxed atmosphere. Thereby, the machine learning activities within MLE, TUHH and in the wider environment shall be made more visible, cooperations shall be promoted and also interested students shall be given an insight.

Abdul Qadir Ibrahim - Parareal with a physics informed neural network as a coarse propagator

Parallel-in-time algorithms provide an additional layer of concurrency for the numerical integration of models based on time-dependent differential equations. Methods like Parareal, which parallelize across multiple time steps, rely on a computationally cheap and coarse integrator to propagate information forward in time, while a parallelizable expensive fine propagator provides accuracy. Typically, the coarse method is a numerical integrator using lower resolution, reduced order or a simplified model. Our paper proposes to use a physics-informed neural network (PINN) instead. We demonstrate for the Black-Scholes equation, a partial differential equation from computational finance, that Parareal with a PINN coarse propagator provides better speedup than a numerical coarse propagator. Training and evaluating a neural network are both tasks whose computing patterns are well suited for GPUs. By contrast, mesh-based algorithms with their low computational intensity struggle to perform well. We show that moving the coarse propagator PINN to a GPU while running the numerical fine propagator on the CPU further improves Parareal's single-node performance. This suggests that integrating machine learning techniques into parallel-in-time integration methods and exploiting their differences in computing patterns might offer a way to better utilize heterogeneous architectures. 

Lectures will be held online via Zoom on Mondays starting at 16:00 in the winter semester 2023 in English. General zoom link for all lectures: Link

The presentation series “Train your engineering network” on diverse topics of Machine Learning addresses all interested persons at TUHH, from MLE partners as well as from the Hamburg region in general and aims at promoting the exchange of information and knowledge between these persons as well as their networking in a relaxed atmosphere. Thereby, the machine learning activities within MLE, TUHH and in the wider environment shall be made more visible, cooperations shall be promoted and also interested students shall be given an insight.

Sebastian Schibsdat & Denys Romanenko - Self-acting anomaly detection and quality estimation for semi-automated drilling with machine learning methods

Due to the high number of rivet holes per aircraft produced, automated process monitoring of the drilling process promises a significant reduction in manual inspection. Advances in sensor technology in new machine tools are greatly expanding the data base. Thus, self-learning can be applied to holistic process monitoring. In this presentation, the authors present approaches to anomaly detection and quality control in the drilling process. Supervised, semi-supervised and unsupervised methods were used for anomaly detection and compared with classical methods of quality control charts. In addition to engineered feature extraction, a new method was used to extract features using a CNN.

Lectures will be held online via Zoom on Mondays starting at 16:00 in the winter semester 2023 in English. General zoom link for all lectures: Link