arXiv:2605.24868v1 Announce Type: new Abstract: Temporal surrogate models are effective for predicting chaotic dynamical systems where computational cost can be prohibitive. Several deep neural network architectures can be used for such purposes. In this work, a few commonly used architectures are compared using a common training protocol. The objective is to fairly assess the impact of model architectures for long-horizon prediction stability. Experiments are carried out for three problems, the double pendulum, the Kuramoto-Sivashinsky equations, and the Kolmogorov flow. The experiments are c
This research is emerging as the capabilities and limitations of AI for complex scientific modeling become clearer, pushing for more robust and reliable predictive tools.
Improved temporal surrogate models can significantly accelerate research and development in fields reliant on complex simulations, reducing computational costs and speeding up discovery.
The ability to accurately and stably predict chaotic systems over long horizons with AI models is incrementally advancing, offering more reliable tools for scientific and engineering applications.
- · AI researchers in scientific computing
- · Physics and engineering R&D sectors
- · Cloud computing providers
- · Deep neural network developers
- · Traditional high-fidelity simulation software providers reliant on brute-force c
More efficient and accurate simulation of chaotic systems is enabled by advanced AI models.
Faster and cheaper iterative design cycles become possible in fields like materials science, climate modeling, and aerospace engineering.
New discoveries or technological breakthroughs, previously constrained by computational expense, might accelerate across scientific disciplines.
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