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Yongquan Qu

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
2024

ChaosBench: A Multi-Channel, Physics-Based Benchmark for Subseasonal-to-Seasonal Climate Prediction

NeurIPS 2024oral

Accurate prediction of climate in the subseasonal-to-seasonal scale is crucial for disaster preparedness and robust decision making amidst climate change. Yet, forecasting beyond the weather timescale is challenging because it deals with problems other than initial condition, including boundary inte…