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This paper shows that the geometric symmetry visible from neural network weights depends on the positional encoding and readout observable, and validates this using MLPs trained on 2D signed distance functions with multiple symmetry groups.
This paper proposes Neural Radiated-Noise Fields (NRNF), a neural network approach for predicting underwater vehicle radiated noise spectra as a continuous function of 3D position, orientation, and frequency. Evaluated on lake trial data, the model achieves an average prediction error of 3.5 dB in the 50–5000 Hz band across multiple generalization settings.
This paper proposes NeTMY, an amortization-free coordinate neural field for inverse problems in NV-center quantum sensing, using a corrected forward model and sparse reconstruction losses to overcome center-collapse pathologies.