Implicit neural representations (INRs) have proven effective in various tasks including image, shape, audio, and video reconstruction. These INRs typically learn the implicit field from sampled input points.

We propose a novel neural network-based approach to modeling protein dynamics using an implicit representation of a protein's surface in 3D and time.

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The first category of works utilizes a neural network structure shared across the signals, which takes a latent code vector2 as an input and modifies the INR output accordingly (e.g.,[36,2,31]).