论文标题
AI早期科学发现助理
AI Research Associate for Early-Stage Scientific Discovery
论文作者
论文摘要
数十年来，人工智能（AI）越来越多地应用于科学活动。但是，它与科学过程中的有见地和值得信赖的合作者相去甚远。大多数现有的AI方法要么太简单了，不能在科学家面临的实际问题中有用，要么在域名（甚至是教条）中，扼杀了变革性发现或范式转移。我们介绍了基于（a）基于物理学的建模的新颖偏见的新颖本体的早期科学发现的AI研究助理，该研究具有上下文感知，可解释和可推广的经典和相对论物理学； （b）自动搜索具有内置不变式的高级（通过域 - 不合稳定构建体）的可行和简约的假设，例如，假定的假定形式的保存原则形式由预设的时空拓扑所暗示的； （c）从稀疏（可能是嘈杂）数据集中，将列举的假设自动汇编自动汇编，以涉及域特异性，可解释和可训练/可训练/可训练的基于张量的计算图，例如构成性或物质定律。
Artificial intelligence (AI) has been increasingly applied in scientific activities for decades; however, it is still far from an insightful and trustworthy collaborator in the scientific process. Most existing AI methods are either too simplistic to be useful in real problems faced by scientists or too domain-specialized (even dogmatized), stifling transformative discoveries or paradigm shifts. We present an AI research associate for early-stage scientific discovery based on (a) a novel minimally-biased ontology for physics-based modeling that is context-aware, interpretable, and generalizable across classical and relativistic physics; (b) automatic search for viable and parsimonious hypotheses, represented at a high-level (via domain-agnostic constructs) with built-in invariants, e.g., postulated forms of conservation principles implied by a presupposed spacetime topology; and (c) automatic compilation of the enumerated hypotheses to domain-specific, interpretable, and trainable/testable tensor-based computation graphs to learn phenomenological relations, e.g., constitutive or material laws, from sparse (and possibly noisy) data sets.
