Research map / Probabilistic and scientific modeling / Molecules and proteins
Learning Composable Energy Surrogates for PDE Order Reduction
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Parameterize log scalar stiffness instead of energy directly (ablation) | Composed energy surrogate · Validation dataset | Validation metrics (E %err, G-sim, Hvp-sim) | Helped |
| Remove rigid body translations from the data via Procrustes analysis (ablation) | Composed energy surrogate · Validation dataset | Validation metrics (E %err, G-sim, Hvp-sim) | No clear effect |
| Train with Sobolev loss on energy gradients (ablation) | Composed energy surrogate · Validation dataset | Validation metrics (E %err, G-sim, Hvp-sim) | Helped |
| Train with Sobolev loss on energy Hessian-vector products (ablation) | Composed energy surrogate · Validation dataset | Validation metrics (E %err, G-sim, Hvp-sim) | Helped |
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