A5063646250- Crystallization and Solubility Studies
- X-ray Diffraction in Crystallography
- Brake Systems and Friction Analysis
- Bladed Disk Vibration Dynamics
- Nonlinear Dynamics and Pattern Formation
- Ocean Waves and Remote Sensing
- Meteorological Phenomena and Simulations
- Cryospheric studies and observations
- Chaos control and synchronization
- Arctic and Antarctic ice dynamics
- Crystallography and molecular interactions
- Model Reduction and Neural Networks
- Oceanographic and Atmospheric Processes
- Vehicle Noise and Vibration Control
- Hydraulic and Pneumatic Systems
- Climate change and permafrost
- Structural Health Monitoring Techniques
- Tribology and Lubrication Engineering
- Dynamics and Control of Mechanical Systems
- Adhesion, Friction, and Surface Interactions
- Vibration Control and Rheological Fluids
- Neural Networks and Reservoir Computing
- Machine Fault Diagnosis Techniques
- Gear and Bearing Dynamics Analysis
- Vibration and Dynamic Analysis
Technische Universität Berlin
2023-2025
Indian Institute of Science Education and Research Mohali
2024
Hamburg University of Technology
2015-2023
Universität Hamburg
2015-2023
University of Technology Sydney
2018-2019
Technology Dynamics (United States)
2017
The quest to understand relationships in networks across scientific disciplines has intensified. However, the optimal network architecture remains elusive, particularly for complex information processing. Therefore, we investigate how and specific structures form efficiently solve distinct tasks using a framework of performance-dependent evolution, leveraging reservoir computing principles. Our study demonstrates that task-specific minimal obtained through this consistently outperform...
The assimilation and prediction of phase-resolved surface gravity waves are critical challenges in ocean science engineering. Potential flow theory (PFT) has been widely employed to develop wave models numerical techniques for prediction. However, traditional methods often limited. For example, most simplified have a limited ability capture strong nonlinearity, while fully nonlinear PFT solvers fail meet the speed requirements engineering applications. This computational inefficiency also...
Abstract Data-based methods have gained increasing importance in engineering. Success stories are prevalent areas such as data-driven modeling, control, and automation, well surrogate modeling for accelerated simulation. Beyond engineering, generative large-language models increasingly helping with tasks that, previously, were solely associated creative human processes. Thus, it seems timely to seek artificial-intelligence-support engineering design automate, help with, or accelerate...
Abstract The pervasiveness of multi-stability in nonlinear dynamical systems calls for novel concepts stability and a consistent quantification long-term behavior. basin is global metric that builds on estimating the attraction volumes by Monte Carlo sampling. computation involves extensive numerical time integrations, attractor characterization, clustering trajectories. We introduce , an open-source software project aims at enabling researchers to efficiently compute their with minimal...
Time recordings of impulse-type oscillation responses are short and highly transient. These characteristics may complicate the usage classical spectral signal processing techniques for (a) describing dynamics (b) deriving discriminative features from data. However, common model identification validation mostly rely on steady-state recordings, characteristic properties non-transient behavior. In this work, a recent method, which allows reconstructing differential equations time series data,...
Abstract In this work, a purely data-driven approach to mapping out the state of dynamical system over set chosen parameters is presented and demonstrated along case study using real-world experimental data from friction brake system. Complex engineering systems often exhibit rich bifurcation behavior with respect one or several parameters, which difficult grasp approaches numerical simulations. At same time, growing need for energy-efficient machines that can operate under varying extreme...
<title>Abstract</title> The quest to understand <italic>structure-function</italic> relationships in networks across scientific disciplines has intensified. However, the optimal network architecture remains elusive, particularly for complex information processing. Therefore, we investigate how and specific structures form efficiently solve distinct tasks using a novel framework of performance-dependent evolution, leveraging reservoir computing principles. Our study demonstrates that...
Some aspects of engineering dynamics, such as nonlinearities and transient motion many interconnected parts, remain difficult to handle today. To comply with increasing demands on resilience safety, the dynamics large machines need be better understood. Complex network methods, already present in scientific disciplines, provide a tool set complementary conventional methods system analysis. This work aims at providing new, function-based view mechanical systems by generating functional...
The measurement of deep water gravity wave elevations using in situ devices, such as gauges, typically yields spatially sparse data due to the deployment a limited number costly devices. This sparsity complicates reconstruction spatio-temporal extent surface elevation and presents an ill-posed assimilation problem, which is challenging solve with conventional numerical techniques. To address this issue, we propose application physics-informed neural network (PINN) reconstruct physically...
Abstract In the age of big data availability, data-driven techniques have been proposed recently to compute time evolution spatio-temporal dynamics. Depending on required a priori knowledge about underlying processes, spectrum black-box end-to-end learning approaches, physics-informed neural networks, and data-informed discrepancy modeling approaches can be identified. this work, we propose purely approach that uses fully convolutional networks learn dynamics directly from parameterized...
Data-driven system identification procedures have recently enabled the reconstruction of governing differential equations from vibration signal recordings. In this contribution, sparse nonlinear dynamics is applied to structural a geometrically system. First, methodology validated against forced Duffing oscillator evaluate its robustness noise and limited data. Then, two weakly coupled cantilever beams with base excitation are reconstructed experimental Results indicate appealing abilities...
Abstract Optimal control of wind farms to maximize power is a challenging task since the wake interaction between turbines highly nonlinear phenomenon. In recent years field Reinforcement Learning has made great contributions problems and been successfully applied optimization in 2D laminar flows. this work, farm for first time authors’ best knowledge. To demonstrate abilities newly developed framework, parameters an already existing strategy, helix approach, are tuned optimize total...