A Physics Informed Neural Network (PINN) is a specialized type of artificial intelligence model that brings together the power of traditional neural networks with the knowledge accumulated over centuries in the field of physics. Neural networks, for those not familiar, are the crux of most modern artificial intelligence systems. They receive input data (like a picture or a sentence), apply a series of calculations to that data, and output a result (like categorizing the picture or translating the sentence). However, traditional neural networks are purely data-driven and do not have any in-built understanding of the physical laws that govern the world.

This is where the term “Physics Informed” plays an important role. These are not just any ordinary neural networks, but ones that are designed and trained in a manner that they bring in the essence of physical laws – laws that explain how the universe works – into their computation and predictive prowess. These laws, like the law of conservation of energy or Newton’s laws of motion, represent validated knowledge that we’ve known for centuries. For example, if the neural network needs to make a prediction about a bouncing ball, making it “Physics Informed” would imply integrating Newton’s laws into the predictive model making it more realistic and accurate.

The implementation is achieved by incorporating these laws as additional layers in the network or as constraints in the loss function (which is a measure of how well the model is performing). This way, not only does the neural network ‘learn’ from the given data, but it also adheres to the physical laws that the system under study should comply with.

Let’s have an easy-to-understand example for semi-technical people. Suppose you’re analyzing the pattern of a pendulum movement. Normal neural networks can learn the movement by training on hundreds of thousands of examples of pendulum swings. But even then, there’s no guarantee that it will perfectly capture the pattern owing to the randomness in the data.

However, if we train a Physics Informed Neural Network on this task, it brings the physical law governing the pendulum (simple harmonic motion in physics) into its learning process. As a result, even with less training data, it can effectively learn the pendulum’s motion pattern and give accurate predictions, because it’s not solely dependent on the data; it also has the ‘knowledge’ of the basic physics behind the pendulum’s movement.

Not only do Physics Informed Neural Networks improve the predictions by incorporating physics, but they also make efficient use of data and possess the ability to extrapolate sensibly, making them more robust and versatile.

A great application of PINNs would be in computational fluid dynamics, a branch of study that deals with predicting the flow of fluid. The traditional way requires solving complex mathematical equations which is computationally intensive and time-consuming. However, using a PINN, the process can be simplified as it inherently understands the physics laws (like the conservation of mass and energy) that govern the fluid dynamics. Therefore, it can predict fluid behavior much faster and even in situations where there is limited data.

This approach of leveraging PINNs creates a bridge between the deterministic world of physical laws and the stochastic nature of learning from data. It’s an intersection of data-driven learning and knowledge-driven learning.

PINNs have an incredibly broad application to varying fields in science, here are a few examples:

1) Weather and Climate Modelling: Accurately predicting weather and climate patterns is a significant challenge due to the complexity of atmospheric physics. The traditional models require immense computational resources and even then often lack precision. PINNs, with their ability to incorporate physical laws like conservation of energy, could provide more accurate predictions, even with less data.

2) Biophysics: PINNs can be beneficial in understanding biological systems. For example, heart dynamics involve fluid-structure interactions that follow physical laws. Simulating these systems using traditional methods is computurally challenging, but PINNs can simplify the process while enhancing accuracy.

3) Quantum Physics: Quantum mechanics is a field of physics that includes complex equations that can be difficult to solve, even with modern computers. PINNs, with their ability to use physical laws as a part of their functioning, could provide a tool to approach quantum mechanical problems in a new, more efficient way.

4) Geophysics: In geophysics, predicting things like seismic activity or oil reservoir simulations can be key to preventing disasters or optimizing resource extraction. PINNs can help simulate such situations more accurately by integrating laws of physics in their computation

5) Astrophysics: Understanding phenomena in outer space, like star formation or black hole dynamics, involves dealing with considerably complex equations of astrophysics. Applying PINNs can significantly speed up these simulations by leveraging the underlying physical laws, thereby speeding up our understanding of the universe.

6) Environmental Science: Predicting the spread of pollutants or understanding the dynamics of an ecosystem are areas where PINNs can provide enormous help. For instance, by integrating the laws of fluid dynamics and thermodynamics, PINNs can simulate and forecast the spread of pollutants in a river or ocean more accurately, thereby aiding in effective environmental management.

7) Aerodynamics: In the field of aerospace engineering, designing the most aerodynamic shapes and predicting the behavior of airflows over aircraft bodies are of utmost importance. These are computationally intensive problems that can be simplified using PINNs, thereby saving cost and time in simulations and ultimately aiding in more efficient aircraft design.

8) Material Science: Discovering new materials or predicting their properties can be done much more efficiently using PINNs. By integrating physical and chemical laws that govern the behavior of materials, PINNs can assist in accelerating the discovery and development of new materials with desired properties.

PINNs can be a transformational tool in any scientific field where data analysis and the understanding of physical laws go hand in hand. By incorporating these laws directly into the model, PINNs provide a remarkable possibility of blending the robustness of physics-based modeling with the adaptivity of machine learning.

PINNs can help us understand complex systems more accurately and efficiently, making gigantic scientific problems tractable with available resources. They act as a bridge between domain knowledge and machine learning, enabling us to leverage data while ensuring the predictions are physically consistent.

In the coming years, as these networks continue to be refined and enhanced, we can expect a significant acceleration in scientific discoveries, technological advancements, and our overall understanding of various scientific phenomena. In essence, PINNs have the potential to revolutionize a whole spectrum of scientific domains.

In my opinion, PINNS are a fascinating development, which allow us to build models that are not just data-dependent, but also have a pre-existing understanding of how our world functions. By doing so, PINNs could enable new breakthroughs in various fields including climate modeling, predicting natural disasters like earthquakes or tsunamis, aerodynamics, and many more areas where physical laws play a crucial role.