Scheduled presentations:

• Facing & mitigating common challenges when working with real-world data: The Data Learning Paradigm
  *R. Arcucci (Imperial College London)
• Feasibility and benefits of integration of Hierarchical Matrices in Deep Neural Network context
  *V. Mele (University of Naples Federico II), L. Carracciuolo (Italian National Research Council - CNR)
• A Simple Machine Learning Approach to the Inverse Problem of Coffee Customisation
  *J. Giacomini (Università degli Studi di Camerino)
• Sparse Identification of Stochastic Differential Equations from Kramers Moyal approximations
  D. Conte (University of Salerno), *I. Santaniello (University of Salerno), D. Breda (University of Udine), M. Tanveer (University of Udine), R. D'Ambrosio (University of L'Aquila)
• Splitting and Matrix Oriented Techniques for Reaction-Diffusion PDEs: Comparisons and Applications
  D. Conte (Università degli Studi di Salerno), *S. Iscaro (Università degli Studi di Salerno), G. Pagano (Università degli Studi di Napoli Federico II), B. Paternoster (Università degli Studi di Salerno)
• Adaptability of KANs as activation functions in classical MLP networks
  P. De Luca (University of Naples Parthenope), *E. Di Nardo (University of Naples Parthenope), A. Ciaramella (University of Naples Parthenope)
• Parameter Estimation for Groundwater Flow Using Physics-Informed Neural Networks with Noise
  *V. Schiano Di Cola (IRSA - CNR), M. Berardi (IRSA - CNR)