DDSP is a library of differentiable versions of common DSP functions (such as synthesizers, waveshapers, and filters). This allows these interpretable elements to be used as part of an deep learning ...

This contains a PyTorch implementation of Differentiable Optimizers with Perturbations in Tensorflow. All credit belongs to the original authors which can be found below. The source code, tests, and ...

The differential equations describing this reaction scheme are For this system, the initial values for the concentrations are derived from equilibrium considerations (as a function of parameters) or ...

A differentiable variant of the Gillespie algorithm enables gradient-based optimization for stochastic chemical kinetics, facilitating efficient parameter estimation and the design of biochemical ...

Real-world analog systems, such as photonic neural networks, intrinsically suffer from noise that can impede model convergence and accuracy for a variety of deep learning models. In the presence of ...

Harmonic functions, defined as twice continuously differentiable functions satisfying Laplace’s equation, have long been a subject of intense study in both pure and applied mathematics. Their ...

In this article we deal with the algebra of generalized Hukuhara differentiable fuzzy functions (gH-differentiable fuzzy functions, for short). We show that the sum and the gH-difference of two ...