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Incorporating Symmetry into Deep Dynamics Models for Improved Generalization

ICLR 2021 · Probabilistic and scientific modeling › Dynamical systems · approach: Physics-informed dynamics learning · 78 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Enforce uniform-motion equivarianceU-net · Rayleigh–Bénard convection, UMRMSE: 2.27 -> 0.71Helped
Enforce uniform-motion equivarianceU-net · Rayleigh–Bénard convection, UMESE: 0.34 -> 0.14Helped
Enforce magnitude equivarianceU-net · Rayleigh–Bénard convection, MagRMSE: 3.59 -> 0.68Helped
Enforce magnitude equivarianceU-net · Rayleigh–Bénard convection, MagESE: 0.55 -> 0.34Helped
Enforce rotational equivarianceU-net · Rayleigh–Bénard convection, RotRMSE: 2.78 -> 0.74Helped
Enforce rotational equivarianceU-net · Rayleigh–Bénard convection, RotESE: 0.91 -> 1.16Hurt
Enforce scale equivarianceU-net · Rayleigh–Bénard convection, ScaleRMSE: 1.65 -> 0.90Helped
Enforce scale equivarianceU-net · Rayleigh–Bénard convection, ScaleESE: 4.25 -> 0.89Helped
Enforce magnitude equivarianceU-net · Ocean currents, Test_timeRMSE: 0.70 -> 0.63Helped
Enforce rotational equivarianceU-net · Ocean currents, Test_timeESE: 0.77 -> 0.42Helped
Enforce scale equivarianceU-net · Ocean currents, Test_domainESE: 0.73 -> 0.43Helped
Enforce equivarianceCNN · Heat diffusion, transformed Mag test setRMSE: 0.103 -> 0.028Helped

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