{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/11"},{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis/3"},{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/3"}],"enrichment":{"capability":"Provides differentiable GPU-capable solvers for controlled differential equations (CDEs), enabling Neural Controlled Differential Equation models for irregular time series with support for backpropagation and memory-efficient adjoint methods.","skillfed_tags":["time-series-modeling","differential-equations","gpu-accelerated"],"use_cases":["Build time-series classifiers that handle irregular sampling, variable lengths, and missing values without preprocessing.","Model financial or sensor data where observations arrive at non-uniform time intervals.","Implement continuous-time recurrent models as an alternative to LSTMs or Transformers for sequential prediction.","Train state-space models where the dynamics are controlled by external signals or learned functions.","Perform adjoint-based training on large time-series datasets where memory efficiency is critical."],"what_it_does":"torchcde is a PyTorch library that implements solvers for controlled differential equations (CDEs), a mathematical framework for modeling systems where the evolution of state depends on an external control signal. It is designed specifically for neural network applications, enabling the construction of Neural Controlled Differential Equation models\u2014continuous-time recurrent architectures that excel at handling irregular, variable-length time series with missing data.\n\nThe library provides two main components: integrators (the `cdeint` function) that solve the CDE system by computing how state evolves under a control signal, and interpolation schemes that construct smooth continuous controls from discrete, irregularly-sampled data. It supports both standard backpropagation through the solver and memory-efficient adjoint-method backpropagation, with configurable backends (torchdiffeq or torchsde) to leverage different solver implementations. GPU acceleration is built in through PyTorch.","worth_installing":"Yes, if you are working on irregular time-series modeling and want a principled continuous-time approach. The library is well-designed and permissively licensed. However, proceed with caution: the project is aging (last release October 2021, no recent commits), so compatibility with the latest PyTorch versions and long-term maintenance are uncertain. Verify that it works with your current environment before committing to production use."},"id":"torchcde","links":{"html":"https://skillfed.io/packages/torchcde","md":"https://skillfed.io/packages/torchcde.md","pypi":"https://pypi.org/project/torchcde/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2021-10-17","license_spdx":null,"license_treatment":"permissive","name":"torchcde","python_support":"supports_current","summary":"Differentiable controlled differential equation solvers for PyTorch with GPU support and memory-efficient adjoint backpropagation."},"popularity":{"monthly_downloads":84351,"position":14004,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.2.5"}