Scheduled presentations:

• Using Machine Learning Interatomic Potentials to Investigate the Nanoscale Frictional Behavior of 2D-Material Heterostructures
  *M. Valderrama (Imperial College), J. Ewen (University Of Bath), D. Dini (Imperial College)
• Comparative Study of Prediction of Incompressible Flow Using Machine Learning
  *Y. Tamura (Toyo University), M. Masuda (Toyo University)
• Operator learning of microstructure-induced random elasticity fields using Neural Operators
  *Y. Arai (Kindai University)
• Study of XAI in Metabolome Estimation
  *A. Mashimo (Toyo University), H. Nishi (Saitama University), D. Yamanaka (Y’s Data Science Co. Ltd.), T. Miyamoto (University of Tsukuba), F. Hakuno (The University of Tokyo), S. Takahashi (The University of Tokyo), Y. Tamura (Toyo University), M. Masuda (Toyo University)
• Proposal of data augmentation method for small-sample quantitative data
  *R. Sakamoto (Toyo University), H. Nishi (Saitama Unversity), D. Yamanaka (Y’s Data Science Co. Ltd.), F. Hakuno (The University of Tokyo), T. Miyamoto (University of Tsukuba), S. Takahashi (The University of Tokyo), Y. Tamura (Toyo University), M. Masuda (Toyo University)
• A Novel Decoupled Framework to Solve Truss Problems Using GNNs
  *P. Yadav (Indian Institute of Technology Madras), G. Bridhani (Indian Institute of Technology Madras), S. Umakanthan (Indian Institute of Technology Madras)