Balanced Neural ODEs: nonlinear model order reduction and Koopman operator approximations
Julius Aka Johannes Brunnemann Jörg Eiden Arne Speerforck Lars Mikelsons
Variational Autoencoders (VAEs) are a powerful framework for learning latent representations of reduced dimensionality, while Neural ODEs excel in learning transient system dynamics. This work combines the strengths of both to generate fast surrogate models with adjustable complexity reacting on time-varying inputs signals. By leveraging the VAE’s dimensionality reduction using a non-hierarchical prior, our method adaptively assigns stochastic noise, naturally complementing known NeuralODE training enhancements and enabling probabilistic
time series modeling. We show that standard Latent ODEs struggle with dimensionality reduction in systems with time-varying inputs. Our approach mitigates this by continuously propagating variational parameters through time, establishing fixed information channels in latent space. This results in a flexible and robust method that can learn different system complexities, e.g. deep neural networks or
linear matrices. Hereby, it enables efficient approximation of the Koopman operator without the need for predefining its dimensionality. As our method balances dimensionality reduction and reconstruction accuracy, we call it Balanced Neural ODE (B-NODE). We demonstrate the effectiveness of this methods on several academic and real-world test cases, e.g. a power plant or MuJoCo data.
Neural ODE, VAE, Dynamical Variational Autoencoder, state space models, Koopman theory, surrogate models, B-NODE, Balanced Neural ODE
We present Balance Neural ODEs, that learn dynamics with control inputs as continous, compact latent representation of adjustable complexity.