Project Details
Description
Granular flows are of significant importance across a wide variety of industries, including amongst others oil and gas, pharmaceutical, mining, food production and metallurgy.
There are huge energetic and monetary losses due to poorly understood granular flow processes which are starting to be addressed by the advent of Industry 4.0.
The project aims for an interdisciplinary collaboration between the two partners in order to develop a machine learning algorithm that is capable of reproducing and predicting granular flows with high computational efficiency.
| Status | Finished |
|---|---|
| Effective start/end date | 01.02.2019 → 31.01.2022 |
Fields of science
- 305 Other Human Medicine, Health Sciences
- 304 Medical Biotechnology
- 102019 Machine learning
- 303 Health Sciences
- 302 Clinical Medicine
- 301 Medical-Theoretical Sciences, Pharmacy
- 102 Computer Sciences
- 106005 Bioinformatics
- 106007 Biostatistics
- 304003 Genetic engineering
- 106041 Structural biology
- 101018 Statistics
- 102010 Database systems
- 106023 Molecular biology
- 102001 Artificial intelligence
- 106002 Biochemistry
- 101004 Biomathematics
- 102004 Bioinformatics
- 102015 Information systems
- 101019 Stochastics
- 102003 Image processing
- 103029 Statistical physics
- 101017 Game theory
- 101016 Optimisation
- 202017 Embedded systems
- 101015 Operations research
- 101014 Numerical mathematics
- 101029 Mathematical statistics
- 101028 Mathematical modelling
- 101026 Time series analysis
- 101024 Probability theory
- 102032 Computational intelligence
- 101027 Dynamical systems
- 102013 Human-computer interaction
- 305907 Medical statistics
- 305905 Medical informatics
- 101031 Approximation theory
- 102033 Data mining
- 305901 Computer-aided diagnosis and therapy
- 102018 Artificial neural networks
- 202037 Signal processing
- 202036 Sensor systems
- 202035 Robotics
JKU Focus areas
- Digital Transformation