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University of California, San Diego
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Unlock 20x faster and more accurate simulations of complex physical systems with a new generative framework that aligns trajectories across discretization scales.
Ditch the equivariant constraints: canonicalization lets you train simpler, faster diffusion models that actually *outperform* equivariant architectures for symmetric generative tasks like 3D molecule design.
