Artificial Intelligence is transforming how we use computing power to solve complex problems. Incorporating AI tools within Physics Based Models is an emerging area which can enable the solution of so far unsolved problems in many application domains, including energy industry. Machine learning has shown promising outlook to several challenging problems in CFD, such as the identification and extraction of hidden features in large-scale flow computations, finding undetected correlations between dynamical features of the flow, and generating synthetic CFD datasets through high-fidelity simulations. These approaches are forming a paradigm shift to change the focus of CFD from time-consuming feature detection to in-depth examinations of such features and enabling deeper insight into the physics involved in complex natural processes. Machine learning has provided numerous opportunities to advance the field of CFD, including to accelerate the computationally expensive direct numerical simulations, to improve turbulence closure modeling and to develop enhanced reduced-order models.

This symposium is designed to stimulate CFD professional in industry and academia by providing a venue to exchange new ideas and discuss challenges and opportunities as well as expose this newly emerging field to energy industry. The purpose of this symposium is to provide comprehensive information and insights regarding the role of AI accelerated physics-based modelling in the energy industry. With a highly impressive agenda and a prestigious lineup of speakers from various sectors, including the industry, academia, and software vendors, this symposium offers a unique opportunity for experts to share their knowledge and experiences.

The agenda will include technical presentations covering a wide range of topics, including but not limited to Scientific Machine Learning, Reduced Order modelling, Physics Based Digital Twin, Multi-Scale Modelling, Physics Informed Neural Network and Hybrid/Fusion Modelling.

Stay tuned for Agenda and more details ....