Showing posts from June, 2020

Machine learning models for physics and engineering

The main objective of physics is to understand nature, and one way to achieve this is to build models of the natural phenomenon of interest. For centuries, physicists have been pushing the limits of our knowledge, and in recent years we have commonly begun to encounter challenges where our physics-driven models are either not accurate enough, or too complex, to be useful. In these situations, machine learning models can give a helping hand.

Physics-guided Neural Networks (PGNNs)

Physics-based models are at the heart of today’s technology and science. Over recent years, data-driven models started providing an alternative approach and outperformed physics-driven models in many tasks. Even so, they are data-hungry, their inferences could be hard to explain and generalization remains to be a challenge. Combining data and physics could reveal the best of both worlds.

AI Insights for Human Intelligence

The comparison between artificial intelligence (AI) and human intelligence has been a heated debate ever since Turing envisioned thinking machines. Today  AI is still not intelligent, nevertheless, we can draw lessons from it for our own intelligence.

Plato, Aristotle and Machine Learning

The School of Athens is one of the most well-known frescoes in the world. It is a Renaissance masterpiece depicting the great philosophers of the classical era, painted by Raphael between 1509–1511. While there is much to discuss in this fresco, I would like to draw your attention to the two central figures: Plato pointing to the skies and his student, Aristotle, pointing towards the earth.