Dr. Sai Nethra Betgeri has developed a new artificial intelligence method that combines machine learning with physics to solve one of the most fundamental equations in science — the advection equation. Using a physics-informed neural network (PINN) built in PyTorch, Dr. Betgeri demonstrated how AI can deliver faster, more accurate solutions to problems that have challenged engineers and physicists for decades.
The advection equation describes how things like heat, pollutants, or waves move through space and time. It is a cornerstone of weather prediction, climate modeling, and aerospace engineering. Traditionally, solving it requires heavy computation and strict stability conditions, but the new approach shows that a neural network can learn the solution while naturally respecting the laws of physics.
Instead of relying only on large datasets, PINNs embed the physics of the problem into the AI model itself. “This makes the network smarter,” Dr. Betgeri explained. “It doesn’t just look for patterns in the data — it understands the rules of nature that govern the system.”
The results are promising:
· The network reproduced the behavior of wave-like solutions with high accuracy.
· It worked well even with limited or noisy data.
· It required less computational overhead compared to traditional numerical methods.
By using PyTorch — a popular open-source AI library — Dr. Betgeri was able to implement automatic differentiation, allowing the network to handle the derivatives required for the advection equation. GPU acceleration made training efficient, paving the way for real-world applications.
Experts say this breakthrough could transform industries where fast, reliable simulations are critical. For example, environmental scientists could use it to forecast pollution spread, aerospace engineers to simulate shock waves, and meteorologists to enhance storm prediction.
“This is an exciting step toward blending physics with AI,” said Dr. Betgeri. “My work shows that deep learning can be more than just data-driven — it can be knowledge-driven, guided by the laws of nature themselves.”
The next challenge? Scaling up. Dr. Betgeri plans to extend this approach to multi-dimensional and nonlinear problems, bringing the power of physics-informed AI closer to tackling real-world systems in civil engineering and pandemic diseases.
As the boundaries between artificial intelligence and physics blur, this research underscores a growing trend: the future of science may lie not in choosing between data or theory, but in combining the best of both.
