Symposium & Workshop
13th International Conference on Bioengineering and Biotechnology (ICBB 2027)
The 13th International Conference on Bioengineering and Biotechnology (ICBB 2027) invites you to propose a symposium/workshop.
The proposed symposium/workshop, if accepted, will be co-located with and under the umbrella of the conference.
Proposals by prospective chairs should be submitted to info[@]bbseries.org and will be evaluated by the Organizing and Scientific Committees. Upon approval, the Organizing Committee will disseminate the news through the conference website and call for papers.
To obtain a high attendance, we kindly ask that prospective chairs also publicize the symposium/workshop and personally contact researchers who they think will be interested in contributing. The minimum number of registered presenters for the symposium/workshop to take place is 7. Should attendance reach this minimum requirement, registration fees for the Chair will be waived.
If you have any questions, please do not hesitate to contact us at info[@]bbseries.org.
AI is transforming the way energy and thermofluid systems are modelled, controlled, and deployed. From scientific machine learning and reduced-order modelling to digital twins and predictive optimisation, AI-enabled approaches are accelerating innovation in energy infrastructure, industrial decarbonisation, thermal energy storage, electrification, and smart thermal systems.
The International Symposium on AI for Energy and Thermofluid Engineering (AI-ETE) is an international forum focused on the application of artificial intelligence (AI), scientific machine learning, and hybrid physic, AI methods to energy, heat transfer, fluid flow and thermofluid engineering. The symposium brings together researchers, engineers, technology developers and industrial practitioners from academia, industry and public institutions to present recent advances, exchange ideas and foster interdisciplinary collaboration. It focuses on the integration of data-driven methods with physical modelling, experiments, and engineering practice for the design, operation, monitoring, and optimisation of next-generation low-carbon energy systems.
The symposium will provide a focused platform for discussion and collaboration across the following thematic areas:
- Scientific machine learning for thermofluid engineering: surrogate modelling, reduced-order modelling, physics-informed neural networks, operator learning
- AI for heat transfer, fluid flow and computational fluid dynamics (CFD): turbulence, multiphase flows, thermal management, combustion and transport phenomena
- AI for energy systems and thermal infrastructure: thermal energy storage, heat pumps, district heating and cooling, renewable energy integration, and energy system optimisation
- Digital twins, monitoring, and predictive control: sensor networks, diagnostics, forecasting, asset performance, and real-time optimisation
- Industrial applications and decarbonisation: process optimisation, manufacturing, energy efficiency, utilities, and smart energy infrastructure
The symposium emphasizes both fundamental advances and real-world engineering applications, with a strong focus on technologies that support the global energy transition and sustainable industrial systems.
- Advance the state of the art in AI-enabled energy and thermofluid engineering
- Foster collaboration between AI researchers, energy engineers, and industrial partners
- Promote the integration of scientific machine learning with physics-based modelling and experimental methods
- Support innovation in low-carbon energy technologies and industrial decarbonisation
- Build an international research and innovation community around AI for energy and thermofluid engineering
The symposium is intended for:
- Advance the state of the art in AI-enabled energy and thermofluid engineering
- Foster collaboration between AI researchers, energy engineers, and industrial partners
- Promote the integration of scientific machine learning with physics-based modelling and experimental methods
- Support innovation in low-carbon energy technologies and industrial decarbonisation
- Build an international research and innovation community around AI for energy and thermofluid engineering
Dr. Yasser Mahmoudi Larimi
The University of Manchester, UK
