# Lecture notes on Physics Informed Neural Networks, Neural Operators, and their applications

Published: 2026-09-17 · Updated: 2026-09-17 · Category: Research

Source page: https://www.sipply.dev/story/lecture-notes-on-physics-informed-neural-networks-neural-4c7e214c · 中文: https://www.sipply.dev/zh/story/lecture-notes-on-physics-informed-neural-networks-neural-4c7e214c

## Summary

The University of Bozen/Bolzano has announced a PhD course focused on Physics Informed Neural Networks (PINNs) and Neural Operators (NOs) for the academic year 2025/2026. This course will emphasize practical implementation using PyTorch, providing students with the necessary skills to apply these advanced computational techniques in various fields, particularly engineering and physics. The curriculum aims to bridge theoretical concepts with real-world applications, enabling students to explore the potential of these innovative methods in solving complex problems.

## Key takeaways

- The course will cover both theoretical and practical aspects of PINNs and NOs.
- Students will gain hands-on experience with PyTorch for implementing these technologies.
- The focus on engineering and physics applications highlights the relevance of AI in scientific research.

## Why it matters

This initiative reflects the growing importance of integrating AI techniques in scientific research and engineering, preparing the next generation of researchers to tackle complex challenges using advanced computational methods.

- **Product managers:** Product managers should note the increasing demand for professionals skilled in AI applications within engineering and physics, as this could influence product development strategies and workforce planning.
- **Developers:** For developers and engineers, this course represents an opportunity to deepen their understanding of cutting-edge AI techniques and their practical applications, enhancing their skill set for future projects.
- **Students and job seekers:** Students and job-seekers can benefit from this course by acquiring specialized knowledge in AI applications, making them more competitive in the job market, especially in fields related to engineering and physics.

## Sources

- [Lecture notes on Physics Informed Neural Networks, Neural Operators, and their applications](https://arxiv.org/abs/2609.17638) (arXiv cs.LG, 2026-09-17)

## 中文

### 关于物理信息神经网络、神经算子及其应用的讲义

博岑/博尔扎诺大学宣布开设2025/2026学年的博士课程，专注于物理信息神经网络（PINNs）和神经算子（NOs）。该课程将强调使用PyTorch的实际实施，为学生提供在各个领域，特别是工程和物理中应用这些先进计算技术所需的技能。课程旨在将理论概念与实际应用相结合，使学生能够探索这些创新方法在解决复杂问题中的潜力。

**要点：**

- 课程将涵盖PINNs和NOs的理论和实践方面。
- 学生将获得使用PyTorch实施这些技术的实践经验。
- 对工程和物理应用的关注突显了AI在科学研究中的相关性。

**为什么重要：** 这一举措反映了在科学研究和工程中整合AI技术日益重要的趋势，为下一代研究人员准备应对复杂挑战的先进计算方法。

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