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Sara Shamekh

2 accepted papers

2026

Strictly Constrained Generative Modeling via Split Augmented Langevin Sampling

ICLR 2026poster

Deep generative models hold great promise for representing complex physical systems, but their deployment is currently limited by the lack of guarantees on the physical plausibility of the generated outputs. Ensuring that known physical constraints are enforced is therefore critical when applying ge…

Cited by 0SourcecodeScholar
2023

ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation

NeurIPS 2023oral

Modern climate projections lack adequate spatial and temporal resolution due to computational constraints. A consequence is inaccurate and imprecise predictions of critical processes such as storms. Hybrid methods that combine physics with machine learning (ML) have introduced a new generation of hi…