Learning coarse-step dynamics and internal mechanical response with graph networks

arXiv:2609.30344 · cs.LG · Submitted 2026-09-24 · Read on arXiv

cs.LG

Submitted: 2026-09-24

Updated: 2026-09-24

Terminology

• Benchmark — 36× in this paper · explained in 2 episodes
• Accuracy — 17× in this paper · explained in 1 episode
• Adaptation — 16× in this paper · explained in 1 episode
• Ablation — 15× in this paper · explained in 1 episode
• Decode — 12× in this paper · explained in 2 episodes
• Anisotropy — 11× in this paper · explained in 3 episodes
• Trace — 10× in this paper · explained in 2 episodes
• Core — 8× in this paper · explained in 2 episodes
• Adam — 4× in this paper · explained in 2 episodes
• Calibration — 3× in this paper · explained in 7 episodes
• Embeddings — 3× in this paper · explained in 2 episodes
• Fidelity — 3× in this paper · explained in 2 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Graph Construction — 3× in this paper · explained in 3 episodes
• International Conference on Machine Learning — 3× in this paper
• Sparsity — 3× in this paper · explained in 4 episodes
• Collective Relational Inference — 2× in this paper
• Equivariant Graph Neural Operator — 2× in this paper
• Latent Representation — 2× in this paper · explained in 2 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Adjacency Matrix — 1× in this paper · explained in 1 episode
• Cluster expansion — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Error Decomposition — 1× in this paper · explained in 3 episodes
• GNNs — 1× in this paper · explained in 2 episodes
• In International Conference on Learning Representations — 1× in this paper
• Information-preserving Graph Neural Simulator — 1× in this paper
• Message-Passing Atomic Cluster Expansion — 1× in this paper
• Physics-encoded Time Integrator Graph Network — 1× in this paper
• Robustness — 1× in this paper · explained in 5 episodes

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks