  Influenced by the advances in data and computing, the scientific practice
increasingly involves machine learning and artificial intelligence driven
methods which requires specialized capabilities at the system-, science- and
service-level in addition to the conventional large-capacity supercomputing
approaches. The latest distributed architectures built around the composability
of data-centric applications led to the emergence of a new ecosystem for
container coordination and integration. However, there is still a divide
between the application development pipelines of existing supercomputing
environments, and these new dynamic environments that disaggregate fluid
resource pools through accessible, portable and re-programmable interfaces. New
approaches for dynamic composability of heterogeneous systems are needed to
further advance the data-driven scientific practice for the purpose of more
efficient computing and usable tools for specific scientific domains. In this
paper, we present a novel approach for using composable systems in the
intersection between scientific computing, artificial intelligence (AI), and
remote sensing domain. We describe the architecture of a first working example
of a composable infrastructure that federates Expanse, an NSF-funded
supercomputer, with Nautilus, a Kubernetes-based GPU geo-distributed cluster.
We also summarize a case study in wildfire modeling, that demonstrates the
application of this new infrastructure in scientific workflows: a composed
system that bridges the insights from edge sensing, AI and computing
capabilities with a physics-driven simulation.
