{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/4"}],"enrichment":{"capability":"Provides PyTorch-based ODE solvers with backpropagation support through the adjoint method, enabling differentiable solutions to ordinary differential equations with constant memory cost.","skillfed_tags":["neural-ode","adjoint-method","differentiable-computing"],"use_cases":["Training neural ODEs where the model is defined as a differential equation and gradients flow through the solver.","Physics-informed neural networks that need to differentiate through ODE solutions with memory efficiency.","Simulating and learning dynamics in continuous-time systems (e.g., bouncing ball, learned physics models).","Sensitivity analysis where you need gradients of ODE solutions with respect to parameters or initial conditions.","GPU-accelerated scientific computing requiring both forward ODE integration and backpropagation."],"what_it_does":"torchdiffeq is a PyTorch library for solving ordinary differential equations (ODEs) with full support for automatic differentiation. It implements the main interface `odeint` which solves initial value problems of the form dy/dt = f(t, y) with y(t_0) = y_0, supporting multiple solver algorithms (Runge-Kutta variants, Adams methods, and others) and GPU execution.\n\nThe key capability is backpropagation through ODE solutions: you can compute gradients with respect to the initial condition, parameters, and time points. For deep learning applications, it provides an adjoint method that trades computation for memory, using O(1) memory during backprop by solving an adjoint ODE in the backward pass. The library also supports event-based termination (stopping when a condition is met) with differentiable event times and states, useful for physics-informed neural networks and other scientific computing tasks.","worth_installing":"Yes, if you need differentiable ODE solving in PyTorch. The library is well-established (6472 stars, top 5000 PyPI), has low install friction, permissive licensing, and no known vulnerabilities. The aging maintenance status (631 days since release) is a minor concern but the repo remains active. Install it when you're building neural ODEs, physics-informed models, or any system requiring gradients through ODE solutions."},"id":"torchdiffeq","links":{"html":"https://skillfed.io/packages/torchdiffeq","md":"https://skillfed.io/packages/torchdiffeq.md","pypi":"https://pypi.org/project/torchdiffeq/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2024-11-21","license_spdx":null,"license_treatment":"permissive","name":"torchdiffeq","python_support":"supports_current","summary":"ODE solvers and adjoint sensitivity analysis in PyTorch."},"popularity":{"monthly_downloads":1067062,"position":4412,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.2.5"}