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Solving High-Dimensional PDEs with Latent Spectral Models

ICML 2023 · Probabilistic and scientific modeling › Differential equations and PDEs · approach: Neural PDE solvers · 13 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add neural spectral block (vs. none) (ablation)LSM · Elasticity-PMSE: 0.0253 -> 0.0218Helped
add neural spectral block (vs. none) (ablation)LSM · DarcyMSE: 0.0075 -> 0.0065Helped
add attention-based projector (vs. removing it) (ablation)LSM · Elasticity-PMSE: 0.0563 -> 0.0218Helped
add multiscale architecture (ablation)LSM · DarcyMSE: 0.0123 -> 0.0065Helped
add patchify operation (ablation)LSM · Elasticity-PMSE: 0.0545 -> 0.0218Helped
replace attention projector with convolution (ablation)LSM · Elasticity-PMSE: 0.0218 -> 0.0236Hurt
replace attention projector with self-attention (ablation)LSM · Elasticity-PMSE: 0.0218 -> 0.0245Hurt
replace attention projector with average pooling (ablation)LSM · Elasticity-PMSE: 0.0218 -> 0.0243Hurt
replace neural spectral block with FNO (ablation)LSM · Elasticity-PMSE: 0.0218 -> 0.0356Hurt
replace trigonometric basis with polynomial basis (ablation)LSM · Elasticity-PMSE: 0.0218 -> 0.0261Hurt
replace neural spectral block with MLP (ablation)LSM · Elasticity-PMSE: 0.0253 -> 0.0249 (MLP) vs 0.0218 fullNo clear effect
LSM hierarchical projection and spectral design overall vs previous SOTALSM · Seven PDE benchmarksMSE relative error reduction: 11.5% average relative gainHelped

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