  Rapid population growth and climate change drive urban renewal and
urbanization at massive scales. New computational methods are needed to better
support urban designers in developing sustainable, resilient, and livable urban
environments. Urban design space exploration and multi-objective optimization
of masterplans can be used to expedite planning while achieving better design
outcomes by incorporating generative parametric modeling considering different
stakeholder requirements and simulation-based performance feedback. However, a
lack of generalizable and integrative methods for urban form generation that
can be coupled with simulation and various design performance analysis
constrain the extensibility of workflows. This research introduces an
implementation of a tensor-field-based generative urban modeling toolkit that
facilitates rapid design space exploration and multi-objective optimization by
integrating with Rhino/Grasshopper ecosystem and its urban analysis and
environmental performance simulation tools. Our tensor-field modeling method
provides users with a generalized way to encode contextual constraints such as
waterfront edges, terrain, view-axis, existing streets, landmarks, and
non-geometric design inputs such as network directionality, desired densities
of streets, amenities, buildings, and people as forces that modelers can weigh.
This allows users to generate many, diverse urban fabric configurations that
resemble real-world cities with very few model inputs. We present a case study
to demonstrate the proposed framework's flexibility and applicability and show
how modelers can identify design and environmental performance synergies that
would be hard to find otherwise
