Deep learning continues to play as a powerful state-of-art technique that has
achieved extraordinary accuracy levels in various domains of regression and
classification tasks, including images, video, signal, and natural language
data. The original goal of proposing the neural network model is to improve the
understanding of complex human brains using a mathematical expression approach.
However, recent deep learning techniques continue to lose the interpretations
of its functional process by being treated mostly as a black-box approximator.
To address this issue, such an AI model needs to be biological and
physiological realistic to incorporate a better understanding of human-machine
evolutionary intelligence. In this study, we compare neural networks and
biological circuits to discover the similarities and differences from various
perspective views. We further discuss the insights into how neural networks
learn from data by investigating human biological behaviors and understandable
justifications.

比较神经网络和生物电路之间的相似和不同，研究探讨了从生物学的行为和可理解的理由中深入了解神经网络从数据中学习的见解。

神经网络的生理机制可解释性

Interpretability of Neural Network With Physiological Mechanisms

We showcase the potential of symbolic regression as an analytic method for
use in materials research. First, we briefly describe the current
state-of-the-art method, genetic programming-based symbolic regression (GPSR),
and recent advances in symbolic regression techniques. Next, we discuss
industrial applications of symbolic regression and its potential applications
in materials science. We then present two GPSR use-cases: formulating a
transformation kinetics law and showing the learning scheme discovers the
well-known Johnson-Mehl-Avrami-Kolmogorov (JMAK) form, and learning the Landau
free energy functional form for the displacive tilt transition in perovskite
LaNiO$_3$. Finally, we propose that symbolic regression techniques should be
considered by materials scientists as an alternative to other
machine-learning-based regression models for learning from data.

本文研究了符号回归作为材料研究的分析方法的潜力。讨论了符号回归的工业应用和其在材料科学中的潜在应用。通过两个案例研究，提出符号回归技术应作为学习数据的材料科学家们用来替代其他基于机器学习的回归模型。