NEMSim: Learning Control-Conditioned Multi-Event Physical Dynamics via Executable Event-Mechanism Priors

arXiv:2609.30718 · cs.LG · Submitted 2026-09-25 · Read on arXiv

cs.LG

Submitted: 2026-09-25

Updated: 2026-09-25

Comments: Main paper: 9 pages, 6 figures, 2 tables. Supplementary material included

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 83× in this paper · explained in 2 episodes
• DeepONet — 46× in this paper · explained in 2 episodes
• Fidelity — 9× in this paper · explained in 2 episodes
• Ablation — 8× in this paper · explained in 1 episode
• Abstraction — 8× in this paper · explained in 1 episode
• Robustness — 6× in this paper · explained in 5 episodes
• Generalization — 5× in this paper · explained in 13 episodes
• Adaptation — 4× in this paper · explained in 1 episode
• Aggregation — 4× in this paper · explained in 1 episode
• Core — 4× in this paper · explained in 2 episodes
• Mechanism Response Compiler — 4× in this paper
• Event Intensity Compiler — 3× in this paper
• Mechanism Attribution Compiler — 3× in this paper
• Provenance — 3× in this paper · explained in 3 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Embeddings — 2× in this paper · explained in 2 episodes
• Kinetic Monte Carlo — 2× in this paper
• Momentum transfer — 2× in this paper · explained in 2 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Architectural Adaptation — 1× in this paper · explained in 1 episode
• Conservation Laws — 1× in this paper · explained in 3 episodes
• Inverse Problem — 1× in this paper · explained in 2 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Neural Surrogates — 1× in this paper · explained in 2 episodes
• PDE residual — 1× in this paper · explained in 2 episodes

Sources

• CFDBench: A Large-Scale Benchmark for Machine Learning Methods in Fluid Dynamics
• Universal Differential Equations for Scientific Machine Learning

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