Congress
Artificial Intelligence, Hybrid Modelization, Paving the way to digital twin – MeCCE Plenary Conference
Thursday 04, 11:30h - 12:30h
| CC3. ROOM 3.11
Pre-registration for the congress
2026-06-04 11:30
2026-06-04 12:30
Europe/Madrid
Artificial Intelligence, Hybrid Modelization, Paving the way to digital twin – MeCCE Plenary Conference
Due to the Industry 4.0 revolution, several process challenges must be tackled: (i) How to quickly develop new models, (ii) how to automatically update them, (iii) how to estimate uncertainties
To address them, IFPEN has developed two strategies based on AI. They depend on the extent of knowledge about the chemical process. If a model structure is known, transfer learning is used. Otherwise, hybrid model strategies are proposed.
IFPEN is conducting several research projects on knowledge transfer methods, particularly Bayesian approaches.
IFPEN has developed a physics-informed hybrid modeling framework that integrates mechanistic knowledge with a neural network through automatic differentiation, differential residual loss minimization, and physical boundary constraints.
CC3. ROOM 3.11
Due to the Industry 4.0 revolution, several process challenges must be tackled: (i) How to quickly develop new models, (ii) how to automatically update them, (iii) how to estimate uncertainties
To address them, IFPEN has developed two strategies based on AI. They depend on the extent of knowledge about the chemical process. If a model structure is known, transfer learning is used. Otherwise, hybrid model strategies are proposed.
IFPEN is conducting several research projects on knowledge transfer methods, particularly Bayesian approaches.
IFPEN has developed a physics-informed hybrid modeling framework that integrates mechanistic knowledge with a neural network through automatic differentiation, differential residual loss minimization, and physical boundary constraints.
Organizers
