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- W4313531523 abstract "Cataloging the complex behaviors of dynamical systems can be challenging, even when they are well-described by a simple mechanistic model. If such a system is of limited analytical tractability, brute force simulation is often the only resort. We present an alternative, optimization-driven approach using tools from machine learning. We apply this approach to a novel, fully-optimizable, reaction-diffusion model which incorporates complex chemical reaction networks (termed Dense Reaction-Diffusion Network or Dense RDN). This allows us to systematically identify new states and behaviors, including pattern formation, dissipation-maximizing nonequilibrium states, and replication-like dynamical structures." @default.
- W4313531523 created "2023-01-06" @default.
- W4313531523 creator A5066737876 @default.
- W4313531523 date "2023-01-02" @default.
- W4313531523 modified "2023-09-26" @default.
- W4313531523 title "Exploring Complex Dynamical Systems via Nonconvex Optimization" @default.
- W4313531523 doi "https://doi.org/10.48550/arxiv.2301.00923" @default.
- W4313531523 hasPublicationYear "2023" @default.
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