Quantization in neural network inference refers to the process of mapping high-precision parameters and activations to lower-precision representations, typically using integer or even binary values.

Complex-valued Hopfield neural networks extend the classical Hopfield model by allowing neuron activations and synaptic weights to assume complex values. This generalisation enables the encoding of ...

The TLE-PINN method integrates EPINN and deep learning models through a transfer learning framework, combining strong physical constraints and efficient computational capabilities to accurately ...