The Princeton Plasma Physics Laboratory's Computational Science Department is pleased to announce. [ https://sites.google.com/d/13WaEf9N_dRV6D56spW_7SebByoF71VQe/p/1u7bBH2T5Z_Dm... | The Artificial Scientist - Leveraging In-transit Machine Learning for Plasma Simulations ] Presented by: Sunita Chandrasekaran , Associate Professor , University of Delaware Thursday, January 30, 2025, 2:45 PM Richard Hawryluk Conference Room, B31 8, [ https://pppl.zoom.us/j/91058597465?pwd=bLV9xHE4WFbznMBZFh45MJl4jiZ1pg.1 | Zoom ] Host: Shantenu Jha Abstract: With the rapid advancements in the computer architecture space, the migration of legacy applications to new architectures remains a continuous challenge. To effectively navigate this ever-evolving hardware landscape, software and toolchains must evolve in tandem, staying ahead of the curve in terms of architectural innovation. While this synchronization between hardware and software is inherently complex, it is essential for fully harnessing the potential of advanced hardware platforms. In this context, a marriage between HPC and AI is gaining increasing prominence. By effectively orchestrating the workflow of HPC and AI, we can not only accelerate scientific progress but also achieve significant gains in computational efficiency. One promising strategy to further optimize large-scale workflows is to stream simulation data directly into machine learning (ML) frameworks. This approach bypasses traditional file system bottlenecks, allowing for the transformation of data in transit— asynchronously with both the simulation process and model training. This talk will explore these strategies in detail, demonstrating the synergy between hardware innovation and software adaptation. Using real-world scientific applications as case study, Plasma-in-Cell on GPU, i.e. PIConGPU will showcase how these techniques can be applied at scale to drive both scientific and computational advancements. Sponsorship of an event does not constitute institutional endorsement of external speakers or views presented.