SciML Lecture Series- Christopher Wentland: Domain decomposition and coupling data-driven models of fluid flows
Venue TBA MI, United StatesMore details will be provided soon!
More details will be provided soon!
Zoom link Abstract: Simulating complex physical systems often requires joining non-uniform subsystems, which may be characterized by different geometries or mesh topologies. Coupling these separate subsystems often relies on time-intensive meshing workflows or empirical coupling models, which may not generalize well across all operational regimes. The Schwarz alternating method proposes to overcome these issues, establishing […]
Speakers TBD
This year’s focus of the Advanced Computational Science & Engineering Showcase (ACES) mini-symposium is connecting advanced algorithms, artificial intelligence (AI), and high-performance computing (HPC) architectures to advance scientific discovery. The event showcases the work of the University of Michigan faculty members at the intersection of AI, HPC, and advanced algorithms. It also includes a panel […]
Join Us at the Scientific Computing Club’s General Meeting! Don’t miss out on a chance to contribute your ideas and help shape the future of our club. Let’s connect, collaborate, and create something amazing together! Where: TBD When: October 22nd, 2024, Tuesday, 5:00 - 6:00 PM Meeting Agenda: TBD
Speakers TBD
Discovery of Cellular Reprogramming Methodology Through Single-cell Foundation Models
PhD in Scientific Computing director Eric Johnsen will speak about opportunities for undergraduate or master's students seeking a graduate education in Computation and Artificial Intelligence for Science and Engineering at the University of Michigan. Food will be provided. Please register to attend.
Vishal Subramanian (Materials Science & Engineering and Scientific Computing): Accelerating Fock exact exchange calculations using Tucker Tensor techniques
Heting Fu (Mechanical Engineering and Scientific Computing): Topology Optimization for Die Casting with Nonplanar Parting Surfaces
Global Geomagnetic Perturbation Forecasting with Quantified Uncertainty using Deep Gaussian Process