Physics-informed Neural Networks (PINNs)

In Siml.ai, we are leveraging the capabilities of Physics Informed Neural Networks (PINNs) to develop robust machine learning models and building high-performance physics simulators, leveraging deep learning techniques to save on time, cost and computational resources.

PINNs are a novel development in machine learning that integrate a priori knowledge about the physics of a problem into the learning process. The importance and benefits of PINNs can be outlined as follows:

1. Extrapolation: PINNs are designed to extrapolate better beyond the range of the training data. This is a significant advantage over traditional machine learning models which often struggle in such condition.
2. Interpretability: The integration of physics into the learning process enhances the interpretability of the model. The parameters learned by the network often have a physical meaning, making the model more understandable.
3. Efficient Training: PINNs can often be trained with less data than traditional machine learning models. The physics of the problem can constrain the possible solutions, making the learning process more efficient and making PINNs valuable in data-scarce situations.
4. Model Robustness: The inherent ability of PINNs to incorporate physics laws into the training process results in models that are more robust to noise and outliers in the training data. This is a significant advantage over traditional machine learning models that can be highly sensitive to such issues.
5. Diverse Real-World Application: The ability of PINNs to leverage the underlying physics of a problem makes them highly applicable in real-world scenarios across different fields. For instance, they can be used in fluid dynamics, heat transfer, structural mechanics, and more.
6. Reduced Need for Feature Engineering: Traditional machine learning models often require extensive feature engineering to perform well, which can be time-consuming and requires domain knowledge. With PINNs, the physics of the problem can naturally guide the learning process, reducing the need for manual feature engineering.