Speaker
Description
SciBmad is a new, fully differentiable software ecosystem for accelerator physics, usable in Julia and/or Python. Full differentiability enables efficient and numerically-reliable optimizations of particle accelerators, in particular with machine learning (ML) methods. One of the most commonly used ML frameworks is PyTorch, which provides powerful, versatile, and straightforward tools for such purposes. We implemented PyTorch bindings with SciBmad, enabling seamless integration of SciBmad’s powerful and differentiable simulations with a standard PyTorch workflow. With these bindings, all of PyTorch’s powerful tools can be used easily with SciBmad. In this paper, we describe the implementation details and present some examples demonstrating SciBmad-PyTorch workflows.
| Paper status | Overdue (needs confirmation): Resubmitted proceeding files received and assigned to an editor. Needs Confirmation from the author. |
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