Bio: Saibal De is a 5th year PhD candidate in Applied and Interdisciplinary Mathematics. His research involves using high-performance computing and novel algorithms for accelerating physics-based simulation frameworks, and developing faithful reduced-order models of black-box high-fidelity simulations.

Abstract: With the advancement of computing software and hardware, physics-based simulations have gained notoriety in many scientific and industrial applications due to their highly accurate prediction capabilities. However, in addition to being computationally expensive, even a single of these high-fidelity simulations produce massive amounts of data. Storing and processing all these data thus requires novel approaches. In this talk, I will present how we can use tensor factorization methods for compressing scientific data, leading to dramatic savings in disk-space usage. Towards the end of the talk, I’ll also touch upon how we can potentially construct reduced-order models out of these compressed datasets.