Memory-Conditioned Diffusion Model for Generalized Langevin Dynamics

arXiv:2609.28371 · stat.ML, math-ph, math.MP · Submitted 2026-09-23 · Read on arXiv

stat.ML, math-ph, math.MP

Submitted: 2026-09-23

Updated: 2026-09-23

Code: https://github.com/mlmathphy/DiffusionMemory_GLE2

Terminology

• Diffusion Model — 10× in this paper · explained in 8 episodes
• Accuracy — 8× in this paper · explained in 1 episode
• Benchmark — 5× in this paper · explained in 2 episodes
• Regularization — 4× in this paper · explained in 3 episodes
• Wasserstein Distance — 3× in this paper · explained in 4 episodes
• Flow Matching — 2× in this paper · explained in 8 episodes
• Langevin Dynamics — 2× in this paper · explained in 2 episodes
• Non-Markovian Dynamics — 2× in this paper · explained in 5 episodes
• Stochastic Dynamics — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Memory Decay — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Temporal Correlations — 1× in this paper · explained in 2 episodes
• US Department of Energy — 1× in this paper

Sources

• Learning reduced systems via deep neural networks with memory
• Capturing non-Markovian dynamics in non-equilibrium stochastic systems using flow matching
• Error estimates of a training-free diffusion model for high-dimensional sampling

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