Continual learning is a problem for artificial neural networks that their
biological counterparts are adept at solving. Building on work using Sparse
Distributed Memory (SDM) to connect a core neural circuit with the powerful
Transformer model, we create a modified Multi-Layered Perceptron (MLP) that is
a strong continual learner. We find that every component of our MLP variant
translated from biology is necessary for continual learning. Our solution is
also free from any memory replay or task information, and introduces novel
methods to train sparse networks that may be broadly applicable.

使用稀疏分布式记忆连接核心神经回路与 Transformer 模型，创造了一种修改的多层感知器，该算法可有效解决人工神经网络所面临的连续学习问题，并广泛适用于训练稀疏网络。

稀疏分布式内存是一种连续学习器

Sparse Distributed Memory is a Continual Learner

The adaptability of the convolutional neural network (CNN) technique for
aerodynamic meta-modeling tasks is probed in this work. The primary objective
is to develop suitable CNN architecture for variable flow conditions and object
geometry, in addition to identifying a sufficient data preparation process.
Multiple CNN structures were trained to learn the lift coefficients of the
airfoils with a variety of shapes in multiple flow Mach numbers, Reynolds
numbers, and diverse angles of attack. This is conducted to illustrate the
concept of the technique. A multi-layered perceptron (MLP) is also used for the
training sets. The MLP results are compared with that of the CNN results. The
newly proposed meta-modeling concept has been found to be comparable with the
MLP in learning capability; and more importantly, our CNN model exhibits a
competitive prediction accuracy with minimal constraints in a geometric
representation.

本文探究了卷积神经网络技术在空气动力元模型任务中的适应性，由此为多变的流动条件和物体几何形状开发了合适的 CNN 架构，并比较了多层感知器的训练结果，发现新提出的元模型概念具有与多层感知器相当的学习能力，尤其是我们的 CNN 模型在几何表示方面有较高的预测准确性。