Currently it is costly and difficult to simulate complicated physical scenarios such as tsunamis, earthquakes, or explosions. The mathematical and physical models that describe these effects are highly complex, and solving these models to produce realistic simulations can often require tremendous computing power. Yet physical simulations and digital twins are becoming increasingly crucial tools for researchers in the NSF Directorate for Computer and Information Science and Engineering (CISE), especially as artificial intelligence (AI) models are starting to interact with the real world. Whether considering models that power self-driving cars, household robots, or industrial design tools, a software platform for "physical intelligence" could allow researchers to create a new generation of innovations guided by mathematics and physics. In light of this, this collaborative project brings together investigators from Vanderbilt University, Georgia Institute of Technology, University of California Davis, and Stanford University to create a new, sustainable, community-driven software platform for physical intelligence. The project consists of several main thrusts to build the proposed platform, COSTA (a Community Open Simulation, Training, and Applications framework). The first thrust focuses on developing highly-optimized graphics processing unit (GPU) implementations of common data structures and algorithms used in physical simulations, such as uniform grids, particles, octrees