๐ฃ๐ต๐๐๐ถ๐ฐ๐-๐๐ฎ๐๐ฒ๐ฑ ๐๐ฒ๐ฒ๐ฝ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด
Standard AI models learn from data patterns. They do not understand the rules of nature.
Physics-based deep learning changes this. You combine neural networks with physical laws. This makes your models smarter and more reliable.
Why use physics in your AI models?
- Accuracy: Models follow laws like gravity or energy conservation.
- Efficiency: You need less data to train the system.
- Reliability: The AI stays within logical bounds.
Standard models often make mistakes because they ignore physics. They suggest solutions that are impossible in the real world.
Physics-informed models prevent these errors. They use equations to guide the learning process. This creates a bridge between data science and physical science.
If you build models for engineering or weather, you need this approach. It moves AI from guessing to understanding.
Source: https://dev.to/paperium/physics-based-deep-learning-59e9
Optional learning community: https://t.me/GyaanSetuAi