SPARC is a physics-constrained spatial machine-learning pipeline that trains an ensemble of geographically-weighted models, validates causal relationships via directed acyclic graphs (DAGs), and simulates "what-if" intervention scenarios with built-in uncertainty quantification. It is designed to be domain-agnostic.
machine-learning geospatial causal-inference directed-acyclic-graph climate-adaptation climate-modeling physics-informed-ml urban-heat climate-resilience forcesmip spatial-machine-learning
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Updated
Jun 12, 2026 - Python