5 papers
LyapuFlow: Controlling Generative Flows with Lyapunov Feedback for Inverse Problems
Minseon Gwak, Hans Hao-Hsun Hsu, Danielle C. Maddix +1
Pretrained flow models are now widely used as generative priors in science and vision, where inference-time guidance enables test-time constraints without retraining. Existing meth…
AREX: Affine-Residual Exponential Integrator for Few-Step Sampling in Flow Matching
Shizheng Lin, Soon Hoe Lim, N. Benjamin Erichson
We introduce AREX, a training-free sampler for pretrained flow matching models that uses the target mean and covariance to capture an analytically tractable part of the sampling dy…
Variational Streaming Flow: Probabilistic Forecasting in Physical Time
Hans Hao-Hsun Hsu, Minseon Gwak, Soon Hoe Lim +2
Probabilistic forecasting is important for predicting complex dynamical systems because intrinsic randomness and incomplete observations can cause the same observed state to evolve…
Zatom-1: Towards a Multimodal Foundation Model for 3D Molecules and Materials
Alex Morehead, Miruna Cretu, Antonia Panescu +14
General-purpose 3D modeling in chemistry encompasses molecules and materials, requiring both generative and predictive capabilities. However, most existing AI approaches are optimi…
HydroDiffusion: Diffusion-Based Probabilistic Streamflow Forecasting with a State Space Backbone
Yihan Wang, Annan Yu, Lujun Zhang +2
Recent advances have introduced diffusion models for probabilistic streamflow forecasting, demonstrating strong early flood-warning skill. However, current implementations rely on…