rtqichen/torchdiffeq

Features

• Memory-Efficient - Uses the adjoint method to compute gradients without storing every intermediate solver state.
• ODE-Based - Calculates gradients through ODE solvers using either direct backpropagation or the adjoint method.
• Adjoint Gradient Calculation - Performs memory-efficient backward passes using an adjoint solver to determine parameter gradients.
• Simulation Gradient Computations - Computes loss function gradients by solving an augmented adjoint system backwards in time to optimize memory.
• Neural Dynamics Models - Trains deep learning networks with continuous depth by integrating dynamics functions over a time interval.
• Neural ODE Frameworks - Provides a framework for building deep learning models that simulate continuous depth by integrating dynamics functions over time.
• Simulation Adjoint Kernels - Provides a memory-efficient adjoint method to compute gradients by solving a second differential equation backwards in time.
• Differentiable ODE Integrators - Computes system trajectories and propagates gradients through adaptive or fixed-step numerical solvers.
• Ordinary Differential Equation Solving - Computes solutions to initial value problems using adaptive or fixed-step ordinary differential equation solvers.
• Initial Value Problem Solvers - Computes the solution to initial value problems using adaptive or fixed-step solvers with configurable tolerances.
• Continuous-Depth Network Simulation - Integrates a dynamics function over a time interval to simulate a neural network containing an infinite number of layers.
• Differentiable Solver Termination - Supports terminating solvers exactly when specific scalar functions cross zero and propagates gradients through the event time.
• Differentiable Event Management - Provides a way to stop solvers based on event functions while propagating gradients through the stop time.
• Differentiable Event Simulation - Stops a differential equation solver when specific event functions trigger and propagates gradients through the event time.
• Event-Based Termination - Implements solver termination triggered by specific event functions with exact gradient computation at the stop time.
• Tensor Computation Graphs - Integrates with PyTorch's automatic differentiation engine to track operations and compute gradients across continuous-time dynamics.
• Integration Error Control - Adjusts adaptive solver step sizes using absolute and relative error tolerances to maintain numerical precision.
• Continuous-Time Density Transformations - Transforms simple probability distributions into complex densities by applying learned continuous-time ordinary differential equations.
• Continuous Normalizing Flows - Transforms simple distributions into complex densities by applying learned continuous-time ordinary differential equations.
• Adaptive-Step Integrators - Implements adaptive stepping to maintain numerical precision by adjusting intervals based on local error estimates.
• رؤية الحاسوب - Neural ordinary differential equations for continuous-depth models.
• Data Processing - Learning neural event functions for ordinary differential equations.
• Neural Differential Equations - Framework for neural ordinary differential equations.
• Data and Graph Processing - Solver for ordinary differential equations.
• أدوات المطور - Differentiable ODE solvers.

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