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Research map / Probabilistic and scientific modeling / Differential equations and PDEs

AutoIP: A United Framework to Integrate Physics into Gaussian Processes

ICML 2022 · Probabilistic and scientific modeling › Differential equations and PDEs · approach: Probabilistic inference for ODEs and SDEs · 3 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add physics through an incomplete equation with a latent sourceAutoIP versus GPR · Nonlinear pendulum, no damping, exact training dataRMSE: 1.354±0.005 -> 0.585±0.017Helped
Use the complete equation instead of an incomplete equation (ablation)AutoIP · Nonlinear pendulum, no damping, exact training dataRMSE: 0.585±0.017 -> 0.416±0.050Helped
Use the complete equation instead of an incomplete equation (ablation)AutoIP · Nonlinear pendulum, damping, noisy training dataRMSE: 0.268 ± 0.013 -> 0.133 ± 0.010Helped
Integrate physics through an incomplete equation with latent sourceAutoIP versus GPR · Nonlinear pendulum, no damping, noisy training dataRMSE: 1.44±0.017 -> 0.691 ±0.030Helped
Place collocation points across the whole trajectory rather than only the training region (ablation)AutoIP motion capture · CMU motion capture, Joint 1RMSE: 1.511 ± 0.007 -> 1.103 ± 0.027Helped
Use random collocation points within the training region instead of training inputs (ablation)AutoIP motion capture · CMU motion capture, Joint 1RMSE: 1.511 ± 0.007 -> 1.489 ± 0.03No clear effect
Integrate the diffusion equation with a latent sourceAutoIP versus GPR · Swiss Jura, Task 1RMSE: 0.299 ± 0.009 -> 0.284 ± 0.011Helped
Use the complete equation instead of an incomplete equation (ablation)AutoIP · Diffusion-reaction systemRMSE: 0.1869 -> 0.1865Helped
Increase PINN collocation points from 100 to 10K (ablation)PINN · Diffusion-reaction systemRMSE: 0.4388 -> 0.0169Helped

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