Machine learning speeds up complex dynamic system simulations accurately
Accelerated surrogate dynamics for dynamical, stochastic system evolution
Artificial Intelligence
Summary
Simulating how complex systems change over time can take a very long time, especially when randomness is involved. The authors found a way to make these simulations much faster by combining machine learning tools that simplify the system’s state and predict its future changes. Their approach keeps track of both short-term and long-term effects to avoid errors building up over time. They tested this approach on three different types of systems and got results that match detailed simulations but much quicker.
What this means in practice
- •For simulation engineers: Run faster simulations for complex systems with uncertainty estimates to design better models and experiments.
- •For environmental modelers: Generate quick and accurate predictions of stochastic natural phenomena using a flexible machine learning surrogate.
Authors
Marco Jochum, Ioannis Kouroudis, Gohar Ali Siddiqui, Taher Amine Hamzaoui, Manuel Gößwein, Alessio Gagliardi
Abstract
Dynamic simulations are an entrenched way of gaining insight into the evolution of system dynamics. Their computational cost however is often prohibitively high, especially in cases of stochastic frameworks. Machine learning algorithms are especially suited as simulation surrogates. Nevertheless, they face some very distinct limitations. Firstly, the sheer dimensionality of these systems, however, precludes the use of traditional time series models who struggle with high dimensional feature spaces. Additionally, traditional time series focus exclusively on either long or short range effects, causing local or global drift given enough time. In this paper, we propose a framework that addresses those limitations. Our framework combines a Variational Autoencoder, with a convolutional or graph basis that reduces the dimensionality of the system. This latent vector is propagated in time using a Temporal Fusion Transformer model, which includes both long range and short range effect encoding, as well as static covariate support. We test our framework on three distinct cases, to prove its robustness and in all three we have achieved practically identical to the simulation results at a fraction of the time. Further, our framework is flexible enough to be adapted to any new system and provides an inbuilt uncertainty quantification for targeted experiment design.