Experts Discuss Latest Advances in Numerical and Machine Learning Techniques for Fluid Dynamics
The Mathematics Department of NIT Jamshedpur recently concluded the "Numerical and Machine Learning Techniques for Fluid Dynamics (NM-Fluids 2024)" workshop, focusing on the latest advancements in the field.
JAMSHEDPUR – The NM-Fluids 2024 workshop, organized by the Mathematics Department of NIT Jamshedpur, aimed to share knowledge on the latest advancements in numerical and machine learning techniques for fluid dynamics.
Over 30 research scholars and faculty members from prestigious institutes across India participated in the workshop.
Dr. Raj Nandkeolyar served as the Convener, with Dr. Rajat Tripathi as the Coordinator and Prof. Ramayan Singh as the Chairman.
Expert speakers from renowned institutions, including IITs and NITs, delivered insightful talks throughout the event.
Prof. Kozi Fukagata from Keiyo University, Japan, presented a notable talk on "Machine Learning Assisted Flow Control Using Convolution Neural Networks."
The workshop highlighted the integration of machine learning with fluid dynamics, emphasizing innovative approaches to flow control.
Participants gained valuable insights into the applications of numerical methods and machine learning in fluid dynamics research.
The event fostered collaboration and knowledge exchange among scholars and experts in the field.
The workshop concluded with the rendition of the National Anthem, marking the end of a productive and enlightening gathering.
Dr. Raj Nandkeolyar, the Convener, expressed his gratitude to all participants and speakers for their contributions.
Dr. Rajat Tripathi, the Coordinator, highlighted the significance of such workshops in advancing research and knowledge.
Prof. Ramayan Singh, the Chairman, commended the efforts of the organizing team and the active participation of attendees.
The successful completion of NM-Fluids 2024 underscored NIT Jamshedpur’s commitment to fostering academic excellence and innovation.
Participants left with a deeper understanding of fluid dynamics and its evolving methodologies, inspired to further their research in the field.
The workshop’s focus on machine learning techniques demonstrated the growing importance of interdisciplinary approaches in modern science.
