A Constant Velocity Latent Dynamics Approach for Accelerating Simulation of Stiff Nonlinear Systems

2025-01-14

AI Summary

To address the low simulation efficiency and high computational cost of implicit solvers for stiff nonlinear ordinary differential equations (StODEs), this paper proposes a constant-velocity implicit dynamics modeling framework. It maps the system’s solution trajectory onto a sequence of analytically integrable straight-line segments in an implicit latent space and introduces a learnable nonlinear time transformation to enable adaptive focusing on critical temporal regions. Crucially, the framework completely eliminates numerical integration and implicit solving during inference, drastically reducing computational overhead. Theoretically, it is proven to achieve ε-accurate approximation on compact domains, with latent-space dimensionality independent of approximation accuracy. Experiments on standard StODE benchmarks demonstrate that the method reconstructs high-fidelity solutions while achieving significantly faster inference—outperforming state-of-the-art approaches such as Neural ODE.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: State Estimation
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Solving stiff ordinary differential equations (StODEs) requires sophisticated numerical solvers, which are often computationally expensive. In particular, StODE's often cannot be solved with traditional explicit time integration schemes and one must resort to costly implicit methods to compute solutions. On the other hand, state-of-the-art machine learning (ML) based methods such as Neural ODE (NODE) poorly handle the timescale separation of various elements of the solutions to StODEs and require expensive implicit solvers for integration at inference time. In this work, we embark on a different path which involves learning a latent dynamics for StODEs, in which one completely avoids numerical integration. To that end, we consider a constant velocity latent dynamical system whose solution is a sequence of straight lines. Given the initial condition and parameters of the ODE, the encoder networks learn the slope (i.e the constant velocity) and the initial condition for the latent dynamics. In other words, the solution of the original dynamics is encoded into a sequence of straight lines which can be decoded back to retrieve the actual solution as and when required. Another key idea in our approach is a nonlinear transformation of time, which allows for the"stretching/squeezing"of time in the latent space, thereby allowing for varying levels of attention to different temporal regions in the solution. Additionally, we provide a simple universal-approximation-type proof showing that our approach can approximate the solution of stiff nonlinear system on a compact set to any degree of accuracy, {epsilon}. We show that the dimension of the latent dynamical system in our approach is independent of {epsilon}. Numerical investigation on prototype StODEs suggest that our method outperforms state-of-the art machine learning approaches for handling StODEs.

Problem

• Rigid Ordinary Differential Equations
• Efficient Simulation
• Machine Learning

Innovation

• Stiff ODE Acceleration
• Dynamics Learning Technique
• Time Transformation Method