Research map / Probabilistic and scientific modeling / Bayesian optimization
Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use learned interface conditions instead of randomly selecting them for each phase | Multi-domain PINN · Poisson | relative L2 error: 0.3004 ± 0.0032 -> 0.01406 ± 9.1099e-5 | Helped |
| Use learned interface conditions instead of randomly selecting them for each phase | Multi-domain PINN · Advection | relative L2 error: 0.03922 ± 0.00919 -> 0.00070 ± 3.2790e-5 | Helped |
| Use learned interface conditions instead of random single-condition selection | Multi-domain PINN · Reaction | relative L2 error: 0.00612 ± 0.000278 -> 0.00099 ± 5.999e-6 | Helped |
| Use learned interface conditions instead of randomly selecting them for each phase | Multi-domain PINN · Burgers | relative L2 error: 0.03486 ± 0.00066 -> 0.00139 ± 3.948e-5 | Helped |
| Use two sequential interface-condition sets rather than one set for the whole training run (ablation) | METALIC-UCB · Poisson | relative L2 error: 0.0245± 0.0002 -> 0.01406 ± 9.1099e-5 | Helped |
| Use two sequential interface-condition sets rather than one set for the whole training run (ablation) | METALIC-UCB · Advection | relative L2 error: 0.00078 ± 3.6771e-5 -> 0.00070 ± 3.2790e-5 | Helped |
| Use two sequential interface-condition sets rather than one set for the whole training run (ablation) | METALIC-UCB · Reaction | relative L2 error: 0.00102 ± 8.945e-6 -> 0.00099 ± 5.999e-6 | No clear effect |
| Use two sequential interface-condition sets rather than one set for the whole training run (ablation) | METALIC-UCB · Burgers | relative L2 error: 0.00161 ± 2.939e-5 -> 0.00139 ± 3.948e-5 | Hurt |
| Use UCB rather than Thompson sampling for online selection (ablation) | METALIC-Seq · Poisson | relative L2 error: 0.01639 ± 9.5384e-5 -> 0.01406 ± 9.1099e-5 | Helped |
| Use UCB rather than Thompson sampling for online selection (ablation) | METALIC-Seq · Advection | relative L2 error: 0.00078 ± 3.6473e-5 -> 0.00070 ± 3.2790e-5 | Helped |
| Use UCB rather than Thompson sampling for online selection (ablation) | METALIC-Single · Reaction | relative L2 error: 0.00204 ± 1.013e-4 -> 0.00102 ± 8.945e-6 | Helped |
| Use UCB rather than Thompson sampling for online selection (ablation) | METALIC-Single · Burgers | relative L2 error: 0.00109 ± 1.306e-5 -> 0.00161 ± 2.939e-5 | Hurt |
Closest papers by meaning
- Sub-Sequential Physics-Informed Learning with State Space Model (ICML 2025 · similarity 0.89)
- Gradient Alignment in Physics-informed Neural Networks: A Second-Order Optimization Perspective (NeurIPS 2025 · similarity 0.89)
- Learning a Neural Solver for Parametric PDEs to Enhance Physics-Informed Methods (ICLR 2025 · similarity 0.88)
- RoPINN: Region Optimized Physics-Informed Neural Networks (NeurIPS 2024 · similarity 0.88)
- Efficient Error Certification for Physics-Informed Neural Networks (ICML 2024 · similarity 0.88)
- Parameterized Physics-informed Neural Networks for Parameterized PDEs (ICML 2024 · similarity 0.88)
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