General introduction Deep learning is a subset of machine learning that focuses on training artificial neural networks with multiple layers to recognize patterns and to simulate the complex decision-making power of the human brain. The use of deep learning has seen a significant increase of popularity and applicability over the last decade. While it serves […]
The AI for Science Bootcamp provides a step-by-step overview of the fundamentals of deep neural networks related to scientific computing and physical systems defined by differential equations.
Overview In recent years, Graph Neural Networks (GNNs) and Transformers have led to numerous breakthrough achievements in a variety of fields such as Natural Language Processing (NLP), chemistry, and physics. By doing away with the need for fixed-size inputs, these architectures significantly extend the scope of problems to which deep learning can be applied. This […]
This project has received funding from the European High-Performance Computing Joint Undertaking (JU) under grant agreement No 101101903. The JU receives support from the Digital Europe Programme and Germany, Bulgaria, Austria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, Greece, Hungary, Ireland, Italy, Lithuania, Latvia, Poland, Portugal, Romania, Slovenia, Spain, Sweden, France, Netherlands, Belgium, Luxembourg, Slovakia, Norway, Iceland.
