Workshop on Simulation and machine learning hybrid modeling
The Deep Learning revolution in the last decade has gradually invaded all scientific fields of Digital Sciences. The field of Numerical Simulation is not an exception, and Deep Learning techniques are gradually being used at all stages of the modelisation, simulation, optimization, and control of physical systems.
The IA2 Program – Artificial Intelligence and Augmented Engineering – at IRT SystemX aims at combining Artificial Intelligence techniques with the methods already deployed by industrial engineering. The HSA project focuses on the hybridization of Simulation with Machine Learning, and organizes its first workshop, featuring one international keynote presentation by Maziar Raissi (University Colorado Boulder), and one invited talk by Emmanuel Franck (Inria Nancy Grand Est). This workshop will also allow researchers of the field to present their most recent works, and we solicit contributed presentations covering various topics in the field, including but not limited to the following:
- Handling and explaining the massive output data of heavy numerical simulations
- Accelerating numerical simulations with Deep / Machine Learning
- Improving the accuracy or the robostuness of simulations with Machine Learning
- Learning to solve ODEs and PDEs
- Discovering mechanistic/behavioral models from data
- Incorporating physical constraints in Deep Learning
The Deep Learning revolution in the last decade has gradually invaded all scientific fields of Digital Sciences. The field of Numerical Simulation is not an exception, and Deep Learning techniques are gradually being used at all stages of the modelisation, simulation, optimization, and control of physical systems.
The IA2 Program – Artificial Intelligence and Augmented Engineering – at IRT SystemX aims at combining Artificial Intelligence techniques with the methods already deployed by industrial engineering. The HSA project focuses on the hybridization of Simulation with Machine Learning, and organizes its first workshop, featuring one international keynote presentation by Maziar Raissi (University Colorado Boulder), and one invited talk by Emmanuel Franck (Inria Nancy Grand Est). This workshop will also allow researchers of the field to present their most recent works, and we solicit contributed presentations covering various topics in the field, including but not limited to the following:
- Handling and explaining the massive output data of heavy numerical simulations
- Accelerating numerical simulations with Deep / Machine Learning
- Improving the accuracy or the robostuness of simulations with Machine Learning
- Learning to solve ODEs and PDEs
- Discovering mechanistic/behavioral models from data
- Incorporating physical constraints in Deep Learning