A5019453359- Electrical and Bioimpedance Tomography
- Reservoir Engineering and Simulation Methods
- Model Reduction and Neural Networks
- Seismic Imaging and Inversion Techniques
- Probabilistic and Robust Engineering Design
- Energy Load and Power Forecasting
- Hydraulic Fracturing and Reservoir Analysis
- Grey System Theory Applications
- Mineral Processing and Grinding
- Drilling and Well Engineering
- Advanced Data Processing Techniques
- Rock Mechanics and Modeling
- Landslides and related hazards
Staats- und Universitätsbibliothek Bremen
2023-2024
University of Bremen
2022-2024
Abstract Recent years have witnessed a growth in mathematics for deep learning—which seeks deeper understanding of the concepts learning with and explores how to make it more robust—and mathematics, where algorithms are used solve problems mathematics. The latter has popularised field scientific machine is applied computing. Specifically, neural network (NN) architectures been developed specific classes partial differential equations (PDEs). Such methods exploit properties that inherent PDEs...
Abstract Classical physical modeling with associated numerical simulation (model-based), and prognostic methods based on the analysis of large amounts data (data-driven) are two most common used for mapping complex processes. In recent years, efficient combination these approaches has become increasingly important. Continuum mechanics in core consists conservation equations that-in addition to always-necessary specification process conditions-can be supplemented by phenomenological material...
Electrical Impedance Tomography (EIT) is a powerful imaging technique with diverse applications, e.g., medical diagnosis, industrial monitoring, and environmental studies. The EIT inverse problem about inferring the internal conductivity distribution of an object from measurements taken on its boundary. It severely ill-posed, necessitating advanced computational methods for accurate image reconstructions. Recent years have witnessed significant progress, driven by innovations in...
A reliable power supply has long been identified as an important economic growth parameter. Electricity load forecasts predict the future behavior of electricity load. Carrying out a forecast is for real-time dispatching power, grid maintenance scheduling, expansion planning, and generation planning depending on forecasting horizon. Most methods used in long-term are regressions limited to predicting peak loads yearly or monthly resolution with low accuracy. In this paper, we propose method...
Classical physical modelling with associated numerical simulation (model-based), and prognostic methods based on the analysis of large amounts data (data-driven) are two most common used for mapping complex processes. In recent years, efficient combination these approaches has become increasingly important. Continuum mechanics in core consists conservation equations that -- addition to always necessary specification process conditions can be supplemented by phenomenological material models....
Recent years have witnessed a growth in mathematics for deep learning--which seeks deeper understanding of the concepts learning with and explores how to make it more robust--and mathematics, where algorithms are used solve problems mathematics. The latter has popularised field scientific machine is applied computing. Specifically, neural network architectures been developed specific classes partial differential equations (PDEs). Such methods exploit properties that inherent PDEs thus better...