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.

通过机器学习工具，提出一种基于优化驱动的方法，应用于包含化学反应网络的反应扩散模型（称为 “Dense RDN”），以识别新的状态和行为，包括模式形成，最大化耗散的非平衡态和类似复制的动态结构。

通过非凸优化探索复杂动态系统

Exploring Complex Dynamical Systems via Nonconvex Optimization

This report presents the evaluation approach developed for the DARPA Big
Mechanism program, which aimed at developing computer systems that will read
research papers, integrate the information into a computer model of cancer
mechanisms, and frame new hypotheses. We employed an iterative, incremental
approach to the evaluation of the three phases of the program. In Phase I, we
evaluated the ability of system and human teams ability to read-with-a-model to
capture mechanistic information from the biomedical literature, integrated with
information from expert curated biological databases. In Phase II we evaluated
the ability of systems to assemble fragments of information into a mechanistic
model. The Phase III evaluation focused on the ability of systems to provide
explanations of experimental observations based on models assembled (largely
automatically) by the Big Mechanism process. The evaluation for each phase
built on earlier evaluations and guided developers towards creating
capabilities for the new phase. The report describes our approach, including
innovations such as a reference set (a curated data set limited to major
findings of each paper) to assess the accuracy of systems in extracting
mechanistic findings in the absence of a gold standard, and a method to
evaluate model-based explanations of experimental data. Results of the
evaluation and supporting materials are included in the appendices.

本文描述了 DARPA 大机制计划开发的评估方法，该计划旨在开发能够读取研究论文、将信息集成到癌症机制的计算机模型中并框架新假说的计算机系统，其评估方法以迭代、增量的方式进行，分别评估了系统和人与模型读取、信息整合成机械型模型、根据大型机制流程自动集成模型的系统提供实验观测的解释的能力。

DARPA 大机制计划 MITRE 评估的最终报告

Final Report on MITRE Evaluations for the DARPA Big Mechanism Program

The lack of predictability of citation-based measures frequently used to
gauge impact, from impact factors to short-term citations, raises a fundamental
question: Is there long-term predictability in citation patterns? Here, we
derive a mechanistic model for the citation dynamics of individual papers,
allowing us to collapse the citation histories of papers from different
journals and disciplines into a single curve, indicating that all papers tend
to follow the same universal temporal pattern. The observed patterns not only
help us uncover basic mechanisms that govern scientific impact but also offer
reliable measures of influence that may have potential policy implications.

研究论文提供一种基于机械模型的引用动态模式，具有预测科学影响力和潜在政策意义的可靠测量方式。