torchsde and torchcde
These two tools are complements, as `torchsde` provides differentiable stochastic differential equation (SDE) solvers while `torchcde` offers differentiable controlled differential equation (CDE) solvers, addressing different mathematical formalisms that can arise in the "neural-differential-equations" domain and potentially be used in conjunction for more complex systems.
About torchsde
google-research/torchsde
Differentiable SDE solvers with GPU support and efficient sensitivity analysis.
This tool helps researchers model systems that change randomly over time by solving stochastic differential equations (SDEs) efficiently. You provide the SDE definition, and it generates the numerical solutions and calculates how sensitive these solutions are to changes in the inputs. It's designed for machine learning researchers working on advanced modeling tasks like latent SDEs or generative adversarial networks.
About torchcde
patrick-kidger/torchcde
Differentiable controlled differential equation solvers for PyTorch with GPU support and memory-efficient adjoint backpropagation.
This tool helps machine learning practitioners build models that analyze and make predictions from irregular time series data, like sensor readings or financial transactions. It takes your raw, potentially messy time-series data and transforms it into a continuous signal, then uses that to train a "Neural Controlled Differential Equation" model. The output is a highly accurate model for tasks like classification or forecasting, even with missing values or unevenly spaced data.
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