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Equivariant Spatio-Temporal Attentive Graph Networks to Simulate Physical Dynamics
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the full ESTAG spatio-temporal equivariant architecture instead of the strongest listed baseline | ESTAG · MD17 (all eight molecules) | MSE: Aspirin: 0.669 -> 0.063; Benzene: 0.106 -> 0.003; Ethanol: 0.245 -> 0.099; Malonaldehyde: 0.427 -> 0.101; Naphthalene: 0 | Helped |
| Use full ESTAG instead of ST_GMN | ESTAG · AdK protein dynamics | MSE: 1.526 -> 1.471 | Helped |
| Use full ESTAG instead of AGL-STAN | ESTAG · CMU Motion Capture Walk | MSE: 0.037 -> 0.040 | Hurt |
| Use full ESTAG instead of EqMotion | ESTAG · CMU Motion Capture Basketball | MSE: 4.893 -> 0.746 | Helped |
| Add EDFT-derived trajectory features (ablation) | ESTAG · MD17, AdK protein dynamics, and CMU Motion Capture | MSE: MD17 Aspirin: 0.079 -> 0.063; protein: 1.490 -> 1.471; motion: 0.053 -> 0.040 | Helped |
| Add ETM attention (ablation) | ESTAG · MD17, AdK protein dynamics, and CMU Motion Capture | MSE: MD17 Aspirin: 0.087 -> 0.063; protein: 1.493 -> 1.471; motion: 0.047 -> 0.040 | Helped |
| Add Euclidean equivariance (ablation) | ESTAG · MD17, AdK protein dynamics, and CMU Motion Capture | MSE: MD17 Aspirin: 0.762 -> 0.063; protein: 2.011 -> 1.471; motion: 0.702 -> 0.040 | Helped |
| Use equivariant temporal pooling instead of simple softmax-weighted past-frame aggregation (ablation) | ESTAG · MD17, AdK protein dynamics, and CMU Motion Capture | MSE: MD17 Aspirin: 0.084 -> 0.063; protein: 1.472 -> 1.471; motion: 0.044 -> 0.040 | Mixed |
| Use learnable frequency filter weights w_k (ablation) | EDFT in ESTAG · MD17 (eight molecules) | MSE: Aspirin: 0.071 -> 0.063; Benzene: 0.003 -> 0.003; Ethanol: 0.102 -> 0.099; Malonaldehyde: 0.104 -> 0.101; Naphthalene: 0 | Helped |
| Set the ESTAG layer count L to 2 rather than 1 (ablation) | ESTAG · Ethanol | MSE: 1.25 -> 0.990 | Helped |
| Increase the number of past timestamps from 3 to 10 (ablation) | ESTAG · Ethanol | MSE: 1.760 -> 0.990 | Helped |
Closest papers by meaning
- Equivariant Graph Neural Operator for Modeling 3D Dynamics (ICML 2024 · similarity 0.91)
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- PhyMPGN: Physics-encoded Message Passing Graph Network for spatiotemporal PDE systems (ICLR 2025 · similarity 0.87)
- Are High-Degree Representations Really Unnecessary in Equivariant Graph Neural Networks? (NeurIPS 2024 · similarity 0.87)
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