Conveners
Artificial Intelligence (AI) - III
- Daniele Bonacorsi (University of Bologna)
Description
During the last decade, Artificial Intelligence (AI) and statistical learning techniques have started to become pervasive in scientific applications, exploring the adoption of novel algorithms, modifying the design principles of application workflows, and impacting the way in which grid and cloud computing services are used by a diverse set of scientific communities. This track aims at discussing problems, solutions and application examples related to this area of research, ranging from R&D activities to production-ready solutions. Topics of interests in this track include: AI-enabled scientific workflows; novel approaches in scientific applications adopting machine learning (ML) and deep learning (DL) techniques; cloud-integrated statistical learning as-a-service solutions; anomaly detection techniques; predictive and prescriptive maintenance; experience with MLOps practices; AI-enabled adaptive simulations; experience on ML/DL models training and inference on different hardware resources for scientific applications.
Domain shift occurs when the distributions of features, underlying behaviours or operational conditions differ between source (training) and target (test) domains, causing models to struggle when applied to data from a different context than the one used for training. To mitigate this, several transfer learning approaches have been proposed to reuse and adapt knowledge acquired in the source...
The increasing complexity and scale of modern data centers generate operational environments where the ability to detect anomalies, anticipate failures, and optimize resource usage is becoming critically important. Recent advances in machine learning and artificial intelligence offer powerful techniques for extracting actionable insights from heterogeneous monitoring data, ranging from logs...
The EO4EU project [1] democratises access to Earth Observation (EO) data by providing a comprehensive platform that caters to a wide spectrum of stakeholders, from researchers to policymakers. The EO4EU platform facilitates the seamless retrieval of EO data and the orchestration of complex computational and machine learning workflows. To this aim, the EO4EU platform integrates a semantic...
Large Scientific Facilities such as synchrotron radiation facility (e.g., BSRF, HEPS) and spallation neutron sources (e.g., CSNS), are generating massive, complex, and heterogeneous datasets continuously during routine operations and scientific experiments. Managing and utilizing the diverse experimental data, along with simulation results and literature-derived information, is presenting a...
