Artificial intelligence has reached a threshold. And physics can help it break new ground

For many years, physicists used their minds to make major breakthroughs and advances in their field. What if artificial Intelligence could aid in these discoveries?

According to a press release from the university, Duke University researchers demonstrated last month that machine learning algorithms can incorporate known physics. The team created a machine-learning algorithm that could deduce the properties and interact with electromagnetic fields. This was a first-of its kind project.

Predicting metamaterial properties

These results were extraordinary. The algorithm was able to predict the metamaterial’s properties better than any previous method and also provided new insights.

Professor of electrical and computer engineering at Duke, Willie Padilla said, “By incorporating existing physics directly into machine learning, the algorithm is able to find solutions with fewer training data and in a shorter time.” While this study was primarily a demonstration that the approach can recreate known solutions it also provided some insight into the inner workings non-metallic metamaterials.

The researchers were focused on finding new insights that were both accurate and logical in their research.

Jordan Malof, an assistant professor of electrical and computer engineering at Duke, stated that neural networks attempt to find patterns in data but sometimes they don’t follow the laws of Physics, making the model it creates unpredictable. We made it impossible for the neural network not to find relationships that might fit the data, but aren’t true by forcing it to follow the laws of Physics.”

This was done by imposing a Lorentz model onto the neural network. This equation describes how an electromagnetic field interacts with the intrinsic properties of a material. However, this was not an easy feat.

Omar Khatib is a postdoctoral researcher in Padilla’s lab. He said that “when you make a neural net more interpretable, which was in some sense what they’ve done here,” it can be more difficult to fine tune. “We had difficulty optimizing the training to learn these patterns.

This model is significantly more efficient

Researchers were surprised to discover that the model performed better than the previous neural networks they had developed for similar tasks. The model required significantly fewer parameters to determine metamaterial properties. The model can even discover new properties on its own.

The researchers are now ready to apply their approach to uncharted territory.

Padilla stated, “Now that we have shown that this is possible, we want this approach to systems where physics remains unknown.”

Malof said that while many people use neural networks to predict the material properties of materials, it is difficult to get enough training data for simulations. This work shows that models don’t require as much data and is therefore useful for everyone.

What do you think?

Written by MANI

𝐇𝐞𝐥𝐥𝐨 𝐟𝐫𝐢𝐞𝐧𝐝𝐬, 𝐦𝐲 𝐧𝐚𝐦𝐞 𝐢𝐬 𝐌𝐚𝐧𝐢𝐤𝐚𝐧𝐝𝐚𝐧. 𝐈 𝐚𝐦 𝐩𝐮𝐭𝐭𝐢𝐧𝐠 𝐚𝐥𝐥 𝐤𝐢𝐧𝐝𝐬 𝐨𝐟 𝐢𝐧𝐭𝐞𝐫𝐞𝐬𝐭𝐢𝐧𝐠 𝐢𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 𝐨𝐧 𝐭𝐡𝐢𝐬 𝐰𝐞𝐛𝐬𝐢𝐭𝐞 𝐚𝐧𝐝 𝐚𝐥𝐥 𝐤𝐢𝐧𝐝𝐬 𝐨𝐟 𝐩𝐨𝐬𝐭𝐬 𝐚𝐛𝐨𝐮𝐭 𝐬𝐭𝐫𝐚𝐧𝐠𝐞 𝐚𝐧𝐝 𝐰𝐨𝐧𝐝𝐞𝐫𝐟𝐮𝐥 𝐚𝐧𝐝 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭 𝐟𝐚𝐜𝐭𝐬. 𝐘𝐨𝐮 𝐜𝐚𝐧 𝐚𝐥𝐬𝐨 𝐟𝐢𝐧𝐝 𝐧𝐞𝐰 𝐚𝐧𝐝 𝐢𝐧𝐭𝐞𝐫𝐞𝐬𝐭𝐢𝐧𝐠 𝐢𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 𝐡𝐞𝐫𝐞. 𝐈 𝐡𝐚𝐯𝐞 𝐛𝐞𝐞𝐧 𝐩𝐫𝐨𝐯𝐢𝐝𝐢𝐧𝐠 𝐢𝐧𝐭𝐞𝐫𝐞𝐬𝐭𝐢𝐧𝐠 𝐢𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧. 𝐈 𝐣𝐮𝐬𝐭 𝐰𝐨𝐫𝐤 𝐨𝐧 𝐦𝐲 𝐰𝐞𝐛𝐬𝐢𝐭𝐞 𝐭𝐡𝐞 𝐰𝐡𝐨𝐥𝐞 𝐭𝐢𝐦𝐞. 𝐈 𝐚𝐥𝐬𝐨 𝐡𝐚𝐯𝐞 𝐚 𝐘𝐨𝐮𝐓𝐮𝐛𝐞 𝐜𝐡𝐚𝐧𝐧𝐞𝐥 𝐰𝐢𝐭𝐡 𝐨𝐯𝐞𝐫 𝟔𝟎𝟎𝟎 𝐩𝐞𝐨𝐩𝐥𝐞 𝐟𝐨𝐥𝐥𝐨𝐰𝐢𝐧𝐠 𝐦𝐞.

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