Scheduled presentations:

• Acceleration of Physics-Based Numerical Simulations of Metal Additive Manufacturing Processes by Combining Intra- and Inter-Layer Data-Driven Prediction Methods
  *D. Kozjek (University of Ljubljana), D. Bračun (University of Ljubljana), P. Podržaj (University of Ljubljana), J. Cao (Northwestern University)
• Fast Thermal History Prediction During Additive Manufacturing Processes Using Geometry-Generalizable Deep Learning
  *L. Salesses (Cenaero), L. Arbaoui (Cenaero), A. François (Cenaero), C. Sainvitu (Cenaero)
• Modeling the Transient Thermal Process for the Blown Powder Directed Energy Deposition Process as a Neural Ordinary Differential Equation
  *P. Vulimiri (University of Pittsburgh), S. Hinnebusch (University of Pittsburgh), D. Aryal (University of Pittsburgh), A. To (University of Pittsburgh)
• Multiscale Coupling of Numerical and Data-Driven Models with HASTE: Hybrid Algorithm using Surrogate Temperature Evaluation
  *G. Wagner (Northwestern University), J. Leonor (Northwestern University)
• Moving beyond forward Euler: advanced explicit schemes to accelerate conduction-based simulations in additive manufacturing
  *S. Essongue (Université de Bordeaux)
• Extended physics-informed neural networks framework for thermal simulation of complex geometries during Directed Energy Deposition
  B. Peng (Imperial College London), *A. Panesar (Imperial College London)