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.

我们提出了一种基于人工智能的科学研究助手，用于物理学建模中极早期的科学发现，它通过物理学建模中既可解释又通用的本体论，自动搜索可行和简约的假设，并将列举的假设自动编译到特定于域的可解释和可训练 / 可测试张量计算图中来学习现象学关系，从而有效地提高了科学家的工作效率。

早期科学发现的 AI 研究助理

AI Research Associate for Early-Stage Scientific Discovery

Application of deep convolutional spiking neural networks (SNNs) to
artificial intelligence (AI) tasks has recently gained a lot of interest since
SNNs are hardware-friendly and energy-efficient. Unlike the non-spiking
counterparts, most of the existing SNN simulation frameworks are not
practically efficient enough for large-scale AI tasks. In this paper, we
introduce SpykeTorch, an open-source high-speed simulation framework based on
PyTorch. This framework simulates convolutional SNNs with at most one spike per
neuron and the rank-order encoding scheme. In terms of learning rules, both
spike-timing-dependent plasticity (STDP) and reward-modulated STDP (R-STDP) are
implemented, but other rules could be implemented easily. Apart from the
aforementioned properties, SpykeTorch is highly generic and capable of
reproducing the results of various studies. Computations in the proposed
framework are tensor-based and totally done by PyTorch functions, which in turn
brings the ability of just-in-time optimization for running on CPUs, GPUs, or
Multi-GPU platforms.

本文介绍了一个基于 PyTorch 的名为 SpykeTorch 的模拟框架，用于模拟深度卷积脉冲神经网络和学习规则，该框架具有高速、通用且基于张量计算的优点。