pADAM is a unified generative framework that learns shared priors across heterogeneous multi-physics families (e.g., scalar diffusion to Navier-Stokes).
arXiv · March 18, 2026 · 2603.16757
The Takeaway
Unlike previous scientific ML models restricted to single-equation domains, pADAM can perform forward prediction, inverse inference, and even identify governing physical laws from sparse snapshots within a single architecture. This represents a move toward true foundation models for the physical sciences.
From the abstract
Generalizing across disparate physical laws remains a fundamental challenge for artificial intelligence in science. Existing deep-learning solvers are largely confined to single-equation settings, limiting transfer across physical regimes and inference tasks. Here we introduce pADAM, a unified generative framework that learns a shared probabilistic prior across heterogeneous partial differential equation families. Through a learned joint distribution of system states and, where applicable, physi