Authors: Peter Messmer (NVIDIA Corporation), George Biros (University of Texas, Oden Institute), Shankaran Sriram (GE Aviation)
Abstract: Digital Twins, virtual representations of objects providing actionable information in actionable time by combining sensor data with surrogate models, have a long, successful history in industry. The recent shift in HPC combining simulation, AI and edge computing is not only an opportunity to apply Digital Twin technology in science, but also to apply massive compute power for digital twins. The goal of this BoF is to bring together digital twin practitioners, computational scientists, middleware developers and HPC resource providers to identify opportunities and challenges in building Digital Twins for science and discuss the impact of HPC in this space.
Long Description: The goal of this BoF is to bring together HPC domain scientists, tool developers and end-users from different industries to identify the scientific impact and technical challenges of digital twins, and to create an inclusive platform and a shared vision of scientific digital twins.
A digital twin is a computational model that evolves over time to persistently represent the structure, behavior, and context of a unique physical system or process. Digital twins are characterized by a dynamic and continuous two-way flow of information between the computational model and the physical system with real-world data streams. The digital twin concept is becoming widely adopted by various industries, including climate research, energy, automobile, biomedical, and aerospace. This BoF specifically considers scientific digital twins, addressing the challenges of applying digital twin technology to scientific use cases. In recent years, high performance computing capacity has increased modeling and simulation needs for digital twins to address complex scientific problems.
In one example, the U.K.’s Atomic Energy Authority (UKAEA) and University of Manchester are using a digital twin simulation platform to connect geographically distributed design teams and gain a deeper understanding of fusion plasma behavior. Another example is Formula One racing industry, which is adopting digital twins for virtual design. With each racing car having 150-200 sensors to collect and feed telemetry data at 0.001 s from racing circuits, a digital twin can help significantly in making in-race strategy decisions.
The above examples are just the tip of the iceberg to unlock the true power of scientific digital twins. This BOF will include academic perspectives on the relevance of digital twins to the HPC community, and will cover scientific digital twin adoption though various industry use cases. The session will help domain scientists reach a consistent understanding of scientific digital twins, their potential, and their constraints. Tool developers will gain a broader perspective of the needs of domain scientists and challenges faced by early adopters. HPC center operators will build an understanding of the different use cases and their impact on the scientific users.
We are lining up a diverse group of speakers ranging from domain users, national research labs, and technology makers. We are likely to invite Rob Akers, Head of advanced computing at UKAEA and Chris Hill, Principal Research Engineer at MIT Earth, Atmospheric and Planetary Sciences. Additionally, we reached out to representatives from Oak Ridge National Laboratory, Sandia National Laboratories, and MIT Lincoln Laboratory. Lastly, we would bring perspectives from a tool maker such as Siemens, ANSYS, or Dassault Systemes.
The informal nature of a BOF is the ideal format to foster cross-disciplinary exchange and kick-start joint activities. After this BOF session, we plan to summarize the discussion and insight gained at his BOF into a blog post, which will be released after the session. In addition, we will discuss the organization of a workshop on this topic at one of the future SC events.
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